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The task of providing a natural language description of graphical information of the image is known as image captioning. As a result, it needs an algorithm to create a series of output words and understand the relations between textual and visual elements. The main goal of this research is to caption the image by extracting the features and detecting the object from the image. Here, the object is detected by employing Deep Embedding Clustering. The features from the input image are extracted such as Local Vector Pattern (LVP), Spider Local Image Features, and some statistical features like mean, variance, standard deviation, kurtosis, and skewness. The extracted features and detected objects are given to image captioning which is exploited by Deep Convolutional Neural Network (Deep CNN). The Deep CNN is trained by using the proposed Adaptive Coati Optimization Algorithm (ACOA). The proposed ACOA is attained by the integration of the Adaptive concept and Coati Optimization Algorithm (COA) and thus the image is captioned. The proposed ACOA achieved maximum values in the training data such as 90.5% of precision, 89.9% of recall 89.1% of F1-Score, 90.4% of accuracy, 90.4% of BELU, and 90.9% of ROUGE. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is a significant contributor to global mortality in our advanced society. The majority of males were attributed to deaths caused by CVD. CVD is the primary cause of mortality. It can be avoided with early detection and accurate diagnosis in its first stages. This research introduces a method for detecting CVD by analysing retinal fundus images. The approach under consideration facilitates disease prediction. The primary procedure involved in retinal vessels is the extraction of tissue data, which is then used for the identification and treatment of CVD. In this step, the retinal pictures are subjected to filtering using a Gaussian filter. The identification of the optic disc is accomplished by the process of binarization and circle fitting, which is then followed by the extraction of statistical data. The segmentation of blood vessels is performed using the Chronological Chef Based Optimisation Algorithm (CCBOA)-based Res-Unet. Subsequently, texture features are extracted. The detection of CVD is achieved by employing a deep neural network (DRN) in conjunction with CCBOA. The CCBOA-based DRN demonstrated exceptional efficiency, achieving the maximum level of accuracy at 89.8%. It also exhibited impressive performance in terms of negative predictive value (NPV) at 86.4%, positive predictive value (PPV) at 86.8%, true negative rate (TNR) at 90.5%, and true positive rate (TPR) at 90.1%. [ABSTRACT FROM AUTHOR]

The field of generative artificial intelligence (AI) is experiencing rapid advancements, impacting a multitude of sectors, from computer vision to healthcare. This paper provides a comprehensive review of generative AI's evolution, significance, and applications, including the foundational architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), autoregressive models, flow‐based models, and diffusion models. We delve into the impact of generative algorithms on computer vision, natural language processing, artistic creation, and healthcare, demonstrating their revolutionary potential in data augmentation, text and speech synthesis, and medical image interpretation. While the transformative capabilities of generative AI are acknowledged, the paper also examines ethical concerns, most notably the advent of deepfakes, calling for the development of robust detection frameworks and responsible use guidelines. As generative AI continues to evolve, driven by advances in neural network architectures and deep learning methodologies, this paper provides a holistic overview of the current landscape and a roadmap for future research and ethical considerations in generative AI. [ABSTRACT FROM AUTHOR]

Investigation on organic particles was limited in the background regions of Yangtze River Delta (YRD) and little information has been obtained on organic particles of Lin'an (LA). In the present study, the morphology, composition, mixing state, and size of organic aerosols with diameter less than 1 µm were characterized at Lin'an from 20 March 2019 to 20 April 2019. In all samples, irregular types of organic matter (OM) particles were high fraction during morning (72.4%), afternoon (59.1%), and evening (52%), and most of them were internally mixed. In our study, we identified a higher fraction of internally mixed particles in evenings (85%), followed by afternoon (81%), and fewer mixed particles in mornings (68%), indicating particle growth during afternoon and evening. Further, the results show that fraction of organic coating particles was higher in evening (27.4%) and afternoon (12%) indicates strong photochemical processes and formation of secondary organic aerosol on the inorganic particles and new particles formation. Our study reveals that biomass burning in the morning and coal burning from heavy industries, power plants, and vehicles in surrounding urban regions in the afternoon and evenings significantly affected background air quality. [ABSTRACT FROM AUTHOR]

We report the results of studies of improved tests for non-linearity based on time series-induced network statistics and surrogate data. We compare results from the network-based statistics with the earlier tests available in the literature and demonstrate the superiority of these tests over the previous tests for several systems. The method we propose is based on constructing a network from a time series and using easily computable parameters of the resulting network such as the average path length, graph density and clustering coefficient as test statistics for the surrogate data test. These statistics are tested for their ability to distinguish between nonlinear processes and linear noise processes, using surrogate data tests on time series obtained from the Rössler system, the Lorenz system, the Henon map, the logistic map and an actual experimental time series of wind speed data, and compared with popularly used time series associated statistics. The network-based statistics are found to distinguish between the nonlinear time series and surrogates derived from the data to a higher degree than the commonly used time series-based statistics, even in the presence of measurement noise and dynamical noise. These statistics may thus prove to be of value in distinguishing between time series derived from nonlinear processes and time series obtained from linearly correlated stochastic processes even in the presence of measurement noise and dynamical noise. The results also show that the efficiency of the network parameters is not exacerbated by the presence of outliers in the given time series. [ABSTRACT FROM AUTHOR]

This paper highlights the variations of surface ozone (O3), total column ozone (TCO), oxides of nitrogen (NO and NO2), carbon monoxide (CO), sulphur dioxide (SO2), ammonia (NH3), volatile organic compounds (Benzene, Tolune, Ethyle Benzene, Xylenes (collectively called BTEX)), particulate matters (PM10 and PM2.5), and meteorological parameters at the time of an annular solar eclipse on 26 December 2019 at Kannur town in Kerala, South India. The maximum solar obscuration has resulted a decrease in solar radiation by 93%, air temperature by 16.3%, wind speed by 36.1% and an increase in relative humidity by 27.1% at this coastal location. Along with the reduction in solar radiation, the concentration of surface O3 (61.5%) and total column O3 (11.8%) have been observed to decrease at the maximum phase of solar eclipse. CO and NO2 concentration were found to be increased by 28.9% and 42.2%, respectively, while NO exhibited its typical diurnal variation. Further, a decrease in concentrations of SO2 by 17.6%, PM10 by 18.5%, and PM2.5 by 11.3% were observed. NH3 and BTEX were found to be higher than 11.3% and 22.6% of the concentrations in control days. All of these deviated parameters could be seen returning to their normal state after completing the eclipse episode. The variation of photodissociation coefficient j(NO2) values were theoretically calculated from the observed data, which shows a good agreement with the model simulated j(NO2) reduction. This is an extensive second observation on the variation of trace pollutants on solar eclipse, after the partial solar eclipse observed on 15 January 2010 at Kannur. [ABSTRACT FROM AUTHOR]

The novel SARS-CoV-2 coronavirus that emerged in the city of Wuhan, China, last year has since become the COVID-19 pandemic across all continents. To restrict the spread of the virus pandemic, the Government of India imposed a lockdown from 25 March 2020. In India, Kannur district was identified as the first "hotspot" of virus transmission and a "triple-lockdown" was implemented for a span of twenty days from 20 April 2020. This article highlights the variations of surface O3, NO, NO2, CO, SO2, NH3, VOC's, PM10, PM2.5 and meteorological parameters at the time of pre-lockdown, lockdown and triple-lockdown days at Kannur town in south India using ground-based analyzers. From pre-lockdown days to triple-lockdown days, surface O3 concentration was found to increase by 22% in this VOC limited environment. NO and NO2 concentrations were decreased by 61% and 71% respectively. The concentration of PM10 and PM2.5 were observed to decline significantly by 61% and 53% respectively. Reduction in PM10 during lockdown and triple-lockdown days enhanced the intensity of solar radiation reaching the lower troposphere, and increased air temperature and reduced the relative humidity. Owing to this, surface O3 production over Kannur was found to have increased during triple-lockdown days. The concentration of CO (67%), VOCs (61%), SO2 (62%) and NH3 (16%) were found to decrease significantly from pre-lockdown days to triple-lockdown days. The air quality index revealed that the air quality at the observational site was clean during the lockdown. [ABSTRACT FROM AUTHOR]

The role of solvent quality in determining the universal material properties of dilute polymer solutions undergoing steady simple shear flow is examined. A bead-spring chain representation of the polymer molecule is used, and the influence of solvent molecules on polymer conformations is modelled by a narrow Gaussian excluded volume potential that acts pairwise between the beads of the chain. Brownian dynamics simulations data, acquired for chains of finite length, and extrapolated to the limit of infinite chain length, are shown to be model independent. This feature of the narrow Gaussian potential, which leads to results identical to a δ-function repulsive potential, enables the prediction of both universal crossover scaling functions and asymptotic behavior in the excluded volume limit. Universal viscometric functions, obtained by this procedure, are found to exhibit increased shear thinning with increasing solvent quality. In the excluded volume limit, they are found to obey power law scaling with the characteristic shear rate β, in close agreement with previously obtained renormalization group results. The presence of excluded volume interactions is also shown to lead to a weakening of the alignment of the polymer chain with the flow direction. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Purpose This paper aims to introduce a systematic computing and analytical procedure that is applied to the co-author network to identify the temporal evolution and growth of research collaborations in the area of e-governance. The empirical analysis of the temporal co-author network can trace the emerging authors and knowledge bursts over time.Design/methodology/approach The study applied social network theory to trace the author collaboration patterns in the domain of e-governance. Analysis of the co-author network using micro and macro parameters was done to trace the temporal evolution of the author collaborations.Findings E-governance is a multi-disciplinary research domain split over streams of management, politics, information technology and electronics. Hence, research collaborations play a significant role in its advancement. The knowledge sharing between individual authors, institutions and groups through research collaborations, resulting in extensive sharing of data, equipment and research methods, has boosted research activities and development in e-governance. In this paper, the authors systematically analyse the current scenario of research collaborations in the area of e-governance using co-author network to estimate its impact on the advancement of the field. The authors also analysed the temporal evolution of the co-author networks, which show remarkable growth of research collaborations in the domain of e-governance from the year 2000.Research limitations/implications The co-author network analysis is only a proxy measure for the analysis of research collaborations. The names of the authors and the university affiliations used in the article are as retrieved from the research repository of Scopus. The degree, citations and other parameters related with authors have scope only within the environment of the co-author network used in the analysis. The criteria used in the study is limited to the degree of research collaborations and the number of co-authored publications in the giant component of the co-author network.Practical implications Institutions, authors and governments can trace and select suitable topics and choose research groups of co-authors over the world for future research collaborations in e-governance. The knowledge about the emerging and most discussed topics gives an overview of the global research trends of e-governance.Social implications The study identified the evolution of creative collaborations in e-governance in the global perspective. The methodology introduced here is helpful to detect the proficient and productive author collaborations and the spectrum of related e-governance research topics associated with them. As the author collaborations can be mapped to the institutional and country-level collaborations, the information is helpful for researchers, institutions and governments to establish the best collaborations in e-governance research based on the author proficiency, collaboration patterns and research topics as per the requirements.Originality/value The paper introduces a novel research methodology using temporal analysis of co-author network to identify the evolution of research patterns and the associated research topics. [ABSTRACT FROM AUTHOR]

We analyze the underlying dynamics at three different latitudes of ionosphere through total electron content‐series for 4 months. The global dynamics reconstructed from the data is shown to be deterministic with low dimensional chaotic attractors at all the locations, and the dynamics is seen to be less complex at the midlatitude station with smaller dimension of correlation (CD) and maximum Lyapunov exponent (MLE). The influence of geomagnetic storms on the ionospheric dynamics is studied, by analyzing the local dynamics during disturbed and quiet periods, reconstructed from segments of total electron content‐series of shorter duration. With positive MLEs and smaller CDs, the local dynamics is seen to be deterministic and chaotic at the low latitude, midlatitude, and high latitude during both the disturbed and quiet days. However, it is interesting to observe that the dynamics is less chaotic and complex during the disturbed days of the entire period of observation compared to the adjacent quiet days at the three locations. This is further evidenced by sample predictions made using nonlinear tools, yielding better predictions during storms. The smaller CD value during disturbed periods indicates that the geomagnetic activities tend to make a few of the variables dominant enough to control the essential dynamics of the system. Further, the MLEs of the disturbed days are found to increase nearly in a linear fashion from beginning to end of the observation period at all the locations considered. This is an indication of the strong influence of geomagnetic activities on the ionosphere and might have caused the restructuring of the ionospheric dynamics. Key Points: The TEC dynamics is seen to be less complex at the midlatitudes with smaller correlation dimension (CD) and maximum Lyapunov exponent (MLE)The local dynamics is deterministic and chaotic at all latitudes during storm and quiet days. It is less chaotic and complex at storm daysA few variables are dominant to control the dynamics during geomagnetic activities. The MLEs of disturbed days increase in a linear fashion [ABSTRACT FROM AUTHOR]

In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed. Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables, with minimalistic preliminary data. The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation. Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories, and it further reduces the need for specialised background. [ABSTRACT FROM AUTHOR]

Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computation-ally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors. [ABSTRACT FROM AUTHOR]

Inflammation is a complex biological response of tissue cells to harmful stimuli including trauma, tissue necrosis, and infections which plays a key role in the pathophysiology of many deadly diseases. In ethnomedicine Drynaria quercifolia fronds are used to treat inflammation as poultice on swellings and as antibacterial, hepatoprotective, and antipyretic agent. Herein, we have evaluated the antioedematous, antiproliferative, and analgesic properties of the ethanolic extract of fertile fronds of D. quercifolia (FF) by standard procedures. Oral administration of FF produced significant inhibition of carrageenan and histamine induced paw oedema in Wistar rats. FF significantly reduced both wet weight and dry weight of granuloma tissue which shows the inhibitory effect on exudative and proliferative phases of inflammation. FF significantly attenuated acute and delayed phases of formalin induced pain, acetic acid-induced writhing, capsaicin-induced nociception, and hot plate test in mice. Phytochemical analysis revealed the presence of coumarins, flavonoids, glycosides, phenolics, saponins, steroids, tannins, and terpenoids. Total phenolic content was 186mg/g equivalent of gallic acid. The HPLC estimation showed flavanone glycoside naringin (1.2%) and its aglycone naringenin (0.02%). The presence of potent anti-inflammatory and analgesic principles in FF and their synergistic action may be the reason for the proposed therapeutic effects. [ABSTRACT FROM AUTHOR]

Fluoride is undoubtedly one of the greatest inventions of dentistry. Its role in the prevention of dental caries is very well established but its role in prevention and control of periodontal disease is obscure. Many epidemiological studies comparing the periodontal status between high fluoride and low fluoride have produced an inconsistent and non-confirmative data. This article comprehensively reviews the available literature regarding the systemic and topical effect of fluorides on gingival and periodontal health. To present an overview the process consisted of research of Pubmed database for the papers published related to fluorides and periodontal health. A total of 31 articles were selected for final reviewing. The data on effect of natural, systemic and topical fluoride on plaque, gingival health and periodontal health was pooled and results from these studies are categorized into neutral effect, positive effect and adverse effect of fluorides on periodontium. From the available data the precise role of fluoride on periodontal health cannot be drawn. Most of the research results are in favor of either no effect or slightly beneficial effect of fluoride on periodontal health. The adverse effects as reported in few of the studies cannot be under-estimated. Most of the study designs are limited to comparison of periodontal status in high and low fluoride areas. More studies aiming to assess the microbial effect of fluoride on plaque are required. [ABSTRACT FROM AUTHOR]

Abstract  We describe here an experimental study on the effect of energy deposition in the flow field of a 120° blunt cone, carried out in a hypersonic shock tunnel. The energy deposition is realised using an electric arc discharge generated between two electrodes placed in the free stream, and various parameters influencing the effectiveness of this technique is studied. The experimental observations suggest that the location of energy deposition has a vital role in dictating the flow structure, with no noticeable effects being produced on the flow field when the discharge was located close to the body (0.416 times body diameter). In addition, the nature of the test gas and the free stream density are also identified as important parameters. In these experiments, a maximum drag reduction of ~50% and ~84% reduction in stagnation point heating rate has been observed as a result of energy addition. The experimental evidence also indicates that the relaxation of the internal degrees of freedom plays a major role in the alteration of the hypersonic blunt body flow structure and that under the specific conditions encountered in our experiments, the energy deposition is not strong enough to create a shock on its own, but the heated region behind the energy source interacts with the blunt body shock resulting in the flow field alteration. [ABSTRACT FROM AUTHOR]

Electrospinning is one of the simplest techniques for obtaining polymer nano fibers. Nanofibers have large surface area to volume ratio and hence have excellent application potential in sensors, filter design etc. Polyaniline (PANI) is the well-known and widely studied conducting polymer, which however, is insoluble in many common organic solvents and hence difficult to process. PANI in its base form is non conductive but it can be made conducting by protonating with an acids such as hydrochloric acid (HCl) or camphor sulphonic acid (CSA). However, it is difficult to electrospin PANI by itself since we need preferably the polymer in solution form. In this study we have formed nanofibers of PANI (CSA) dispersed in Poly Methyl Methacrylate (PMMA) solution in chloroform. The morphology of the electrospun conducting PMMA-PANI composite fibers is studied using Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). The DC and AC conductivities of these fibers are measured and the results are discussed. [ABSTRACT FROM AUTHOR]

Atmospheric ozone (O3) in the surface level plays a central role in determining air quality and atmospheric oxidizing capacity. In this paper, we review our comprehensive results of simultaneous measurements of surface ozone (O3) and its precursor gas (NOx) and weather parameters that were carried out continuously for a span of six years (January 2013–December 2018) at a typical rural coastal site, Kannur (11.9° N, 75.4° E) in South India. Surface O3 concentration reached its maximum during daytime hours and minimum during the night time. The influence of solar radiation and water content on variations of O3 are discussed. A Multi-Layer Perceptron (MLP) artificial neural network technique has been used to understand the effect of atmospheric temperature on the increase in O3 over the past six years. This has been found that temperature has been a major contributor to the increase in O3 levels over the years. The National Centre for Atmospheric Research- Master Mechanism (NCAR-MM) Photochemical box model study was conducted to validate the variations of O3 in different seasons and years, and the results were shown to be in good agreement with observed trends. [ABSTRACT FROM AUTHOR]

Fetal dystocia was diagnosed clinically in a free ranging spotted deer. The maldispostioned dead fetus was corrected and relieved after chemical restraining. The animal recovered successfully and was later released in free range. [ABSTRACT FROM AUTHOR]