Background: Increased relative wall thickness in hypertensive left ventricular hypertrophy (LVH) has been shown by echocardiography to allow preserved shortening at the endocardium despite depressed LV midwall circumf...Background: Increased relative wall thickness in hypertensive left ventricular hypertrophy (LVH) has been shown by echocardiography to allow preserved shortening at the endocardium despite depressed LV midwall circumferential shortening (MWCS). Depressed MWCS is an adverse prognostic indicator, but whether this finding reflects reduced global or regional LV myocardial function, as assessed by three-dimensional (3D) myocardial strain, is unknown. Methods and Results: Cardiac Magnetic Resonance (CMR) tissue tagging permits direct evaluation of regional 3D intramyocardial strain, independent of LV geometry. We evaluated 21 hypertensive patients with electrocardiographic LVH in the LIFE study and 8 normal controls using 3D MR tagging and echocardiography. Patients had higher MR LV mass than normals (116 ± 40 versus 63 ± 6 g/m2, P = 0.002). Neither echocardiographic fractional shortening (32 ± 6 versus 33% ± 3%), LVEF (63% versus 64%) or mean end-systolic stress (175 ± 27 versus 146 ± 28 g/cm2) were significantly different, yet global MWCS was decreased by both echocardiography (13.4 ± 2.8 versus 18.2% ± 1.5%, P P P = 0.002) in LVH and greater in lateral and anterior regions versus septal and posterior regions ( P P P 0.60, P = 0.001 for both). Conclusions: In patients with hypertensive LVH, despite normal LV function via echocardiography or CMR, CMR intramyocardial tagging show depressed global MWCS while 3D MR strain revealed marked underlying regional heterogeneity of LV dysfunction.展开更多
An oxidation resistance study has been made on Nb-Cr-V-W-Ta high entropy alloy in a range of temperature from 600 to 1400℃in air.Static oxidation study has been performed for either(a)12 or 24 h of heating time or(b)...An oxidation resistance study has been made on Nb-Cr-V-W-Ta high entropy alloy in a range of temperature from 600 to 1400℃in air.Static oxidation study has been performed for either(a)12 or 24 h of heating time or(b)3 or 10℃/min heating rates to the desired oxidation temperature.Cyclic oxidation study conducted for three and a half days has been conducted at 600,700,and 800℃using 12 h of heating cycles.The alloy can withstand the cyclic oxidation process with only a reasonable loss of alloy.The identification of oxides indicates crystals of W and Ta oxides in cylindrical form while Nb and Cr oxides show a nodular or granular morphology at both 1000 and 1200℃while and additional of oxide of V in whisker forms at 1200℃.展开更多
The present study investigated the influence of substrate temperature(Ts)and working pressure(P(Ar))on tailoring the properties of nanocrystalline(nc)molybdenum(Mo)films fabricated by radio-frequency magnetron sputter...The present study investigated the influence of substrate temperature(Ts)and working pressure(P(Ar))on tailoring the properties of nanocrystalline(nc)molybdenum(Mo)films fabricated by radio-frequency magnetron sputtering.The structural,morphological,electrical and optical properties of nc-Mo films were evaluated in detail.The Mo films exhibited(110)orientation with average crystallite size varying from 9 to 22(±1)nm on increasing Ts.Corroborating with structural data,the electrical resistivity decreased from 55μΩcm to 10μΩcm,which is the lowest among all the Mo films.For Mo films deposited under variable P(Ar).the(110)peak intensity decrement coupled with peak broadening on increasing P(Ar).Lower deposition pressure yielded densely packed thin films with superior structural properties along with low resistivity of 15μΩcm.Optimum conditions to produce high quality Mo films with excellent structural,morphological,electrical and optical characteristics for utilization in solar cells as back contact layers were identified.展开更多
Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin...Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.展开更多
Objective:To investigate the hepatoprotective potential of Sida cordata（Malvaceae）（S.cordata） in experimental rats to validate its traditional claim.Methods:Wister albino rats were divided into 6 groups:Croup 1 se...Objective:To investigate the hepatoprotective potential of Sida cordata（Malvaceae）（S.cordata） in experimental rats to validate its traditional claim.Methods:Wister albino rats were divided into 6 groups:Croup 1 served as control;CroupⅡserved as hepatotoxic（CCl4 treated） group; CroupⅢ,Ⅳand V served as（100,200 and 400 mg/kg b.w.） S.cordata leaf extract（SCLE） treated groups;CroupⅥserved as positive control（Silymarin） treated group.Liver marker enzymes serum glutamate oxyloacetic transaminase,serum glutamic pyruvic transaminase,pancreatic enzymatic antioxidants superoxide dismutase（SOD）,lipid peroxidation,catalase（CAT）,reduced glutathione（CSH） were measured and compared along with histopathological studies.Results: Obtained results show that the treatment with SCI.E significantly（P【0.05-【0.001） and dosedependently reduced CCl4 induced elevated serum level of hepatic enzymes.Furthermore, SCLE significantly（up to P【0.001） reduced the lipid peroxidation in the liver tissue and restored activities of defence antioxidant enzymes CSH,SOD and CAT towards normal levels,which was confirmed by the histopathological studies.Conclusions:The results of this study strongly indicate the protective effect of SCLE against CCl4 induced acute liver toxicity in rats and thereby scientifically support its traditional use.展开更多
This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange.To achieve the objectives,the study uses descriptive statistics;tests including var...This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange.To achieve the objectives,the study uses descriptive statistics;tests including variance ratio,Augmented Dickey-Fuller,Phillips-Perron,and Kwiatkowski Phillips Schmidt and Shin;and Autoregressive Integrated Moving Average(ARIMA).The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series,using the ARIMA model.The results reveal that the mean returns of both indices are positive but near zero.This is indicative of a regressive tendency in the longterm.The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values,with few deviations.Hence,the ARIMA model is capable of predicting medium-or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.展开更多
Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information.However,predicting the closing prices of stock indices remains a ...Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information.However,predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity.This paper proposes a novel condensed polynomial neural network(CPNN)for the task of forecasting stock closing price indices.We developed a model that uses partial descriptions(PDs)and is limited to only two layers for the PNN architecture.The outputs of these PDs along with the original features are fed to a single output neuron,and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm.The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices:the BSE,DJIA,NASDAQ,FTSE,and TAIEX.In comparative testing,the proposed model proved its ability to provide closing price predictions with superior accuracy.Further,the Deibold-Mariano test justified the statistical significance of the model,establishing that this approach can be adopted as a competent financial forecasting tool.展开更多
Advanced Metering Infrastructure(AMI)forms an important part in Smart Grids.Routing the data effectively from smart meters to the Edge/Fog node requires an efficient routing protocol.Routing Protocol for Low Power Los...Advanced Metering Infrastructure(AMI)forms an important part in Smart Grids.Routing the data effectively from smart meters to the Edge/Fog node requires an efficient routing protocol.Routing Protocol for Low Power Lossy Area Network(RPL)is a standard routing protocol for IPv6 over Low Power Personal Area Network(6LoWPAN).In a Power Distribution system all the smart meters together form 6LoWPAN network.They communicate with the fog router,which acts as the 6LoWPAN gateway.ContikiRPL was evaluated using Cooja Network simulator for a power distribution network topology.The nodes which were far away from the fog node gave low Packet Delivery Ratio(PDR)and large End to End delay.This paper proposes an aggregation RPL scheme by modifying the existing Contiki RPL.The smart meter nodes communicate to the aggregator,which communicates to the fog node.The results show that the aggregation scheme has 35.6%increase in PDR,lesser hop count and 13.24%decrease in End to End delay on an average compared to existing RPL.展开更多
Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selecti...Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.展开更多
The magnetocaloric effect in the A-site doping colossal magnetoresistance material (La_(0.6)Dy_(0.1))Sr_(0.3)MnO_3 was studied. From the measurement and calculation of isothermal magnetization (M-H) curves under vario...The magnetocaloric effect in the A-site doping colossal magnetoresistance material (La_(0.6)Dy_(0.1))Sr_(0.3)MnO_3 was studied. From the measurement and calculation of isothermal magnetization (M-H) curves under various temperatures, a large magnetocaloric effect with ferromagnetic-paramagnetic transition, additional magnetism exchange action introduces additional magnetic entropy change was discovered. This result suggests that (La_(0.6)Dy_(0.1))Sr_(0.3)MnO_3 is a suitable candidate as working substance at room temperature in magnetic refrigeration technology.展开更多
Cardiovascular magnetic resonance is a non-invasive imaging modality which is emerging as important tool for the investigation and management of pediatric cardiovascular disease. In this review we describe the key tec...Cardiovascular magnetic resonance is a non-invasive imaging modality which is emerging as important tool for the investigation and management of pediatric cardiovascular disease. In this review we describe the key technical and practical differences between scanning children and adults, and highlight some important considerations that must be taken into account for this patient population. Using case examples commonly seen in clinical practice, we discuss the important clinical applications of cardiovascular magnetic resonance, and briefly highlight key future developments in this field.展开更多
In the present study,rare earth samarium(Sm^(3+))substituted Ni-Cu spinel ferrites with the composition of Ni_(0.1)Cu_(0.9)Sm_(x)Fe_(2-x)O_(4)(0≤x≤0.05 in steps of 0.01)were synthesized by using the citrate induced ...In the present study,rare earth samarium(Sm^(3+))substituted Ni-Cu spinel ferrites with the composition of Ni_(0.1)Cu_(0.9)Sm_(x)Fe_(2-x)O_(4)(0≤x≤0.05 in steps of 0.01)were synthesized by using the citrate induced sol-gel auto combustion technique.These ferrites'structural,optical,magnetic,and dielectric studies were carried out using X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),field emission scanning electron microscopy(FESEM),ultraviolet-visible(UV-vis),a vibrational sample magnetometer(VSM),and an LCR meter.The pure Ni-Cu ferrite exhibits a tetragonal structure owing to the presence of the John Tellar ion(Cu^(2+)).XRD patterns confirm that the tetragonal structure gradually transforms into the cubic spinel structure with samarium substitution.The nano-scale structures of these ferrites were confirmed by the average crystallite size(10.11-20.99 nm)derived from the X-ray diffraction patterns,and grain size(42.60-83.36 nm)assessed from FESEM photographs.The existence of elements according to their chemical composition was verified by using energy dispersive X-ray(EDX)spectra.The absorption bands(v_(1) and v_(2))detected in FTIR transmission spectra below the wavenumber of 600 cm^(-1)reveal the stretching vibrations of M-O bonds in the spinel structure at tetrahedral and octahedral locations.The band gap ene rgy obtained from UV absorption reveals the semiconducting nature of the samples.The high saturation magnetization(M_(s))is noticed at 15 K temperature for x=0.02 composition as 32.98 emu/g,while at 300 K for x=0.01composition as 27.61 emu/g.The suggested cation distribution is in good agreement with observed and predicted magnetic moment values at 300 K.The expected behavior of ferrites reveals the observed dielectric constant,loss tangent,and ac-conductivity values in the frequency range of 20 Hz-20 MHz.Cole-Cole plots confirm that the impedance contribution is attributed to grain boundaries.展开更多
Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification m...Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.展开更多
In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While tra...In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisfield or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.展开更多
Microstructure and tribological properties of copper-based hybrid nanocomposites reinforced with copper coatedmultiwalled carbon nanotubes (MWCNTs) and silicon carbide (SiC) were studied. Carbon nanotube was varied fr...Microstructure and tribological properties of copper-based hybrid nanocomposites reinforced with copper coatedmultiwalled carbon nanotubes (MWCNTs) and silicon carbide (SiC) were studied. Carbon nanotube was varied from 1% to 4% withsilicon carbide content being fixed at 4%. The synthesis of copper hybrid nanocomposites involves ball milling, cold pressing andsintering followed by hot pressing. The developed hybrid nanocomposites were subjected to density, grain size, and hardness tests.The tribological performances of the nanocomposites were assessed by carrying out dry sliding wear tests using pin-on-steel disctribometer at different loads. A significant decrease in grain size was observed for the developed hybrid composites when comparedwith pure copper. An improvement of 80% in the micro-hardness of the hybrid nanocomposite has been recorded for 4% carbonnanotubes reinforced hybrid composites when compared with pure copper. An increase in content of CNTs in the hybridnanocomposites results in lowering of the friction coefficient and wear rates of hybrid nanocomposites.展开更多
Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechno...Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.展开更多
In the present work,a solution-based co-precipitation method has been adopted to synthesize pure and cobalt-doped ZnS quantum dots and characterized by XRD,SEM,TEM with EDX,FTIR and gas sensing properties.XRD analysis...In the present work,a solution-based co-precipitation method has been adopted to synthesize pure and cobalt-doped ZnS quantum dots and characterized by XRD,SEM,TEM with EDX,FTIR and gas sensing properties.XRD analysis has shown a single phase of ZnS quantum dots having a zinc blend structure.TEM and XRD line broadening indicated that the average crystallite size in the sample is in the range of 2 to 5 nm.SEM micrographs show spherical-shaped quantum dots.FTIR studies show that cobalt has been successfully doped into the ZnS cubic lattice.EDX spectra have analyzed the elemental presence in the samples and it is evident that the spectra confirmed the presence of cobalt(Co),zinc(Zn),oxygen(O),and sulphur(S)elements only and no other impurities are observed.The ZnS-based quantum dot sensors reveal high sensitivity towards 50 ppm of ammonia vapors at an operating temperature of 70℃.Hence,ZnS-based quantum dots can be a promising and quick traceable sensor towards ammonia sensing applications with good response and recovery time.展开更多
AIM: To investigate the association between mutations in oligomerisation domain 2/caspase recruitment domains 15 (NOD2/CARD15) and the natural history of Crohn’s disease (CD) to identify patients who would...AIM: To investigate the association between mutations in oligomerisation domain 2/caspase recruitment domains 15 (NOD2/CARD15) and the natural history of Crohn’s disease (CD) to identify patients who would benefit from early aggressive medical intervention.展开更多
Sparrow criterion of resolution is used for assessment of the resolution of two object points of apodized optical systems under incoherent illumination of light. Semicircular arrays of circular aperture with discrete ...Sparrow criterion of resolution is used for assessment of the resolution of two object points of apodized optical systems under incoherent illumination of light. Semicircular arrays of circular aperture with discrete asymmetric apodization have suppressed side-lobes and a narrower central peak in the image plane termed as PSF good side on alternatively the right and left of the strong spectral point facilitates to detect the presence of weak spectral point in the vicinity of bright spectral point. The results of investigations on optimum discrete pupil function with semicircular arrays on the intensity distributions in the composite image of two object points with widely varying in their intensities under various degree of coherence of illumination have been studied. Sparrow resolution limits and the dip in central intensity as function of degree of coherence of the illumination (γ), intensity ratio (α), degree of asymmetric apodization (b) and number of discrete elements in semicircular array (n). The efficiency of aperture functions is discussed in terms of these parameters. Pupil function capabilities in redistribution of energy in composite image of two object points in close vicinity have been verified for different considerations. Current study has found an improvement in two-point resolution characteristics compared to their unapodized counter part. Fourier analytical properties of an optical system are presented for evaluation of this practical problem.展开更多
文摘Background: Increased relative wall thickness in hypertensive left ventricular hypertrophy (LVH) has been shown by echocardiography to allow preserved shortening at the endocardium despite depressed LV midwall circumferential shortening (MWCS). Depressed MWCS is an adverse prognostic indicator, but whether this finding reflects reduced global or regional LV myocardial function, as assessed by three-dimensional (3D) myocardial strain, is unknown. Methods and Results: Cardiac Magnetic Resonance (CMR) tissue tagging permits direct evaluation of regional 3D intramyocardial strain, independent of LV geometry. We evaluated 21 hypertensive patients with electrocardiographic LVH in the LIFE study and 8 normal controls using 3D MR tagging and echocardiography. Patients had higher MR LV mass than normals (116 ± 40 versus 63 ± 6 g/m2, P = 0.002). Neither echocardiographic fractional shortening (32 ± 6 versus 33% ± 3%), LVEF (63% versus 64%) or mean end-systolic stress (175 ± 27 versus 146 ± 28 g/cm2) were significantly different, yet global MWCS was decreased by both echocardiography (13.4 ± 2.8 versus 18.2% ± 1.5%, P P P = 0.002) in LVH and greater in lateral and anterior regions versus septal and posterior regions ( P P P 0.60, P = 0.001 for both). Conclusions: In patients with hypertensive LVH, despite normal LV function via echocardiography or CMR, CMR intramyocardial tagging show depressed global MWCS while 3D MR strain revealed marked underlying regional heterogeneity of LV dysfunction.
基金support from the National Science Foundation with NSF-PREM grant#DMR-1827745.
文摘An oxidation resistance study has been made on Nb-Cr-V-W-Ta high entropy alloy in a range of temperature from 600 to 1400℃in air.Static oxidation study has been performed for either(a)12 or 24 h of heating time or(b)3 or 10℃/min heating rates to the desired oxidation temperature.Cyclic oxidation study conducted for three and a half days has been conducted at 600,700,and 800℃using 12 h of heating cycles.The alloy can withstand the cyclic oxidation process with only a reasonable loss of alloy.The identification of oxides indicates crystals of W and Ta oxides in cylindrical form while Nb and Cr oxides show a nodular or granular morphology at both 1000 and 1200℃while and additional of oxide of V in whisker forms at 1200℃.
基金supported financially by the National Science Foundation (NSF) with the NSF-PREM grant#DMR-1827745
文摘The present study investigated the influence of substrate temperature(Ts)and working pressure(P(Ar))on tailoring the properties of nanocrystalline(nc)molybdenum(Mo)films fabricated by radio-frequency magnetron sputtering.The structural,morphological,electrical and optical properties of nc-Mo films were evaluated in detail.The Mo films exhibited(110)orientation with average crystallite size varying from 9 to 22(±1)nm on increasing Ts.Corroborating with structural data,the electrical resistivity decreased from 55μΩcm to 10μΩcm,which is the lowest among all the Mo films.For Mo films deposited under variable P(Ar).the(110)peak intensity decrement coupled with peak broadening on increasing P(Ar).Lower deposition pressure yielded densely packed thin films with superior structural properties along with low resistivity of 15μΩcm.Optimum conditions to produce high quality Mo films with excellent structural,morphological,electrical and optical characteristics for utilization in solar cells as back contact layers were identified.
文摘Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.
文摘Objective:To investigate the hepatoprotective potential of Sida cordata（Malvaceae）（S.cordata） in experimental rats to validate its traditional claim.Methods:Wister albino rats were divided into 6 groups:Croup 1 served as control;CroupⅡserved as hepatotoxic（CCl4 treated） group; CroupⅢ,Ⅳand V served as（100,200 and 400 mg/kg b.w.） S.cordata leaf extract（SCLE） treated groups;CroupⅥserved as positive control（Silymarin） treated group.Liver marker enzymes serum glutamate oxyloacetic transaminase,serum glutamic pyruvic transaminase,pancreatic enzymatic antioxidants superoxide dismutase（SOD）,lipid peroxidation,catalase（CAT）,reduced glutathione（CSH） were measured and compared along with histopathological studies.Results: Obtained results show that the treatment with SCI.E significantly（P【0.05-【0.001） and dosedependently reduced CCl4 induced elevated serum level of hepatic enzymes.Furthermore, SCLE significantly（up to P【0.001） reduced the lipid peroxidation in the liver tissue and restored activities of defence antioxidant enzymes CSH,SOD and CAT towards normal levels,which was confirmed by the histopathological studies.Conclusions:The results of this study strongly indicate the protective effect of SCLE against CCl4 induced acute liver toxicity in rats and thereby scientifically support its traditional use.
文摘This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange.To achieve the objectives,the study uses descriptive statistics;tests including variance ratio,Augmented Dickey-Fuller,Phillips-Perron,and Kwiatkowski Phillips Schmidt and Shin;and Autoregressive Integrated Moving Average(ARIMA).The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series,using the ARIMA model.The results reveal that the mean returns of both indices are positive but near zero.This is indicative of a regressive tendency in the longterm.The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values,with few deviations.Hence,the ARIMA model is capable of predicting medium-or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.
文摘Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information.However,predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity.This paper proposes a novel condensed polynomial neural network(CPNN)for the task of forecasting stock closing price indices.We developed a model that uses partial descriptions(PDs)and is limited to only two layers for the PNN architecture.The outputs of these PDs along with the original features are fed to a single output neuron,and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm.The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices:the BSE,DJIA,NASDAQ,FTSE,and TAIEX.In comparative testing,the proposed model proved its ability to provide closing price predictions with superior accuracy.Further,the Deibold-Mariano test justified the statistical significance of the model,establishing that this approach can be adopted as a competent financial forecasting tool.
基金National Funding from the FCT- Fundacao Para a Ciencia e a Tecnologia through the UID/ EEA/50008/2019 Project by Brazilian National Council for Scientific and Technological Development via Grant No. 309335/2017-5
文摘Advanced Metering Infrastructure(AMI)forms an important part in Smart Grids.Routing the data effectively from smart meters to the Edge/Fog node requires an efficient routing protocol.Routing Protocol for Low Power Lossy Area Network(RPL)is a standard routing protocol for IPv6 over Low Power Personal Area Network(6LoWPAN).In a Power Distribution system all the smart meters together form 6LoWPAN network.They communicate with the fog router,which acts as the 6LoWPAN gateway.ContikiRPL was evaluated using Cooja Network simulator for a power distribution network topology.The nodes which were far away from the fog node gave low Packet Delivery Ratio(PDR)and large End to End delay.This paper proposes an aggregation RPL scheme by modifying the existing Contiki RPL.The smart meter nodes communicate to the aggregator,which communicates to the fog node.The results show that the aggregation scheme has 35.6%increase in PDR,lesser hop count and 13.24%decrease in End to End delay on an average compared to existing RPL.
文摘Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.
基金Project supported by the National Science Foundation of China (10334090) and the National Key Fundamental Research De velopment Program (001CB610604)
文摘The magnetocaloric effect in the A-site doping colossal magnetoresistance material (La_(0.6)Dy_(0.1))Sr_(0.3)MnO_3 was studied. From the measurement and calculation of isothermal magnetization (M-H) curves under various temperatures, a large magnetocaloric effect with ferromagnetic-paramagnetic transition, additional magnetism exchange action introduces additional magnetic entropy change was discovered. This result suggests that (La_(0.6)Dy_(0.1))Sr_(0.3)MnO_3 is a suitable candidate as working substance at room temperature in magnetic refrigeration technology.
基金Supported by NIHR Biomedical Research UnitRoyal Brompton and Harefield NHS Foundation Trust and Imperial College London
文摘Cardiovascular magnetic resonance is a non-invasive imaging modality which is emerging as important tool for the investigation and management of pediatric cardiovascular disease. In this review we describe the key technical and practical differences between scanning children and adults, and highlight some important considerations that must be taken into account for this patient population. Using case examples commonly seen in clinical practice, we discuss the important clinical applications of cardiovascular magnetic resonance, and briefly highlight key future developments in this field.
基金Kamala Institute of Technology and Science for their support and encouragementCSIR-New Delhi for providing financial assistance (09/132 (0879)/2018-EMR-1)+1 种基金CSIR-New Delhi for providing financial assistanceCMR College of Engineering and Technology for their support and encouragement。
文摘In the present study,rare earth samarium(Sm^(3+))substituted Ni-Cu spinel ferrites with the composition of Ni_(0.1)Cu_(0.9)Sm_(x)Fe_(2-x)O_(4)(0≤x≤0.05 in steps of 0.01)were synthesized by using the citrate induced sol-gel auto combustion technique.These ferrites'structural,optical,magnetic,and dielectric studies were carried out using X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),field emission scanning electron microscopy(FESEM),ultraviolet-visible(UV-vis),a vibrational sample magnetometer(VSM),and an LCR meter.The pure Ni-Cu ferrite exhibits a tetragonal structure owing to the presence of the John Tellar ion(Cu^(2+)).XRD patterns confirm that the tetragonal structure gradually transforms into the cubic spinel structure with samarium substitution.The nano-scale structures of these ferrites were confirmed by the average crystallite size(10.11-20.99 nm)derived from the X-ray diffraction patterns,and grain size(42.60-83.36 nm)assessed from FESEM photographs.The existence of elements according to their chemical composition was verified by using energy dispersive X-ray(EDX)spectra.The absorption bands(v_(1) and v_(2))detected in FTIR transmission spectra below the wavenumber of 600 cm^(-1)reveal the stretching vibrations of M-O bonds in the spinel structure at tetrahedral and octahedral locations.The band gap ene rgy obtained from UV absorption reveals the semiconducting nature of the samples.The high saturation magnetization(M_(s))is noticed at 15 K temperature for x=0.02 composition as 32.98 emu/g,while at 300 K for x=0.01composition as 27.61 emu/g.The suggested cation distribution is in good agreement with observed and predicted magnetic moment values at 300 K.The expected behavior of ferrites reveals the observed dielectric constant,loss tangent,and ac-conductivity values in the frequency range of 20 Hz-20 MHz.Cole-Cole plots confirm that the impedance contribution is attributed to grain boundaries.
文摘Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.
文摘In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisfield or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.
文摘Microstructure and tribological properties of copper-based hybrid nanocomposites reinforced with copper coatedmultiwalled carbon nanotubes (MWCNTs) and silicon carbide (SiC) were studied. Carbon nanotube was varied from 1% to 4% withsilicon carbide content being fixed at 4%. The synthesis of copper hybrid nanocomposites involves ball milling, cold pressing andsintering followed by hot pressing. The developed hybrid nanocomposites were subjected to density, grain size, and hardness tests.The tribological performances of the nanocomposites were assessed by carrying out dry sliding wear tests using pin-on-steel disctribometer at different loads. A significant decrease in grain size was observed for the developed hybrid composites when comparedwith pure copper. An improvement of 80% in the micro-hardness of the hybrid nanocomposite has been recorded for 4% carbonnanotubes reinforced hybrid composites when compared with pure copper. An increase in content of CNTs in the hybridnanocomposites results in lowering of the friction coefficient and wear rates of hybrid nanocomposites.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/209/42)This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Path of Research Funding Program.
文摘Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.
文摘In the present work,a solution-based co-precipitation method has been adopted to synthesize pure and cobalt-doped ZnS quantum dots and characterized by XRD,SEM,TEM with EDX,FTIR and gas sensing properties.XRD analysis has shown a single phase of ZnS quantum dots having a zinc blend structure.TEM and XRD line broadening indicated that the average crystallite size in the sample is in the range of 2 to 5 nm.SEM micrographs show spherical-shaped quantum dots.FTIR studies show that cobalt has been successfully doped into the ZnS cubic lattice.EDX spectra have analyzed the elemental presence in the samples and it is evident that the spectra confirmed the presence of cobalt(Co),zinc(Zn),oxygen(O),and sulphur(S)elements only and no other impurities are observed.The ZnS-based quantum dot sensors reveal high sensitivity towards 50 ppm of ammonia vapors at an operating temperature of 70℃.Hence,ZnS-based quantum dots can be a promising and quick traceable sensor towards ammonia sensing applications with good response and recovery time.
文摘AIM: To investigate the association between mutations in oligomerisation domain 2/caspase recruitment domains 15 (NOD2/CARD15) and the natural history of Crohn’s disease (CD) to identify patients who would benefit from early aggressive medical intervention.
文摘Sparrow criterion of resolution is used for assessment of the resolution of two object points of apodized optical systems under incoherent illumination of light. Semicircular arrays of circular aperture with discrete asymmetric apodization have suppressed side-lobes and a narrower central peak in the image plane termed as PSF good side on alternatively the right and left of the strong spectral point facilitates to detect the presence of weak spectral point in the vicinity of bright spectral point. The results of investigations on optimum discrete pupil function with semicircular arrays on the intensity distributions in the composite image of two object points with widely varying in their intensities under various degree of coherence of illumination have been studied. Sparrow resolution limits and the dip in central intensity as function of degree of coherence of the illumination (γ), intensity ratio (α), degree of asymmetric apodization (b) and number of discrete elements in semicircular array (n). The efficiency of aperture functions is discussed in terms of these parameters. Pupil function capabilities in redistribution of energy in composite image of two object points in close vicinity have been verified for different considerations. Current study has found an improvement in two-point resolution characteristics compared to their unapodized counter part. Fourier analytical properties of an optical system are presented for evaluation of this practical problem.
