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Despite an abundance of scientific evidence supporting the effectiveness of COVID-19 vaccines, there has been a recent global surge in vaccine hesitancy, primarily driven by the spread of misinformation on social media platforms. It is crucial to address this issue and raise awareness about the importance of vaccination in combating the deadly COVID-19 virus. Predicting community sentiment through social media platforms can provide valuable insights into vaccine hesitancy, aiding health workers and medical professionals in taking necessary precautionary measures. However, the lack of high-quality labeled data presents a challenge for building an effective COVID-19 sentiment classifier. Additionally, the available labeled datasets suffer from severe class imbalance. To address these challenges, this article presents an effective COVID-19 sentiment prediction framework. Firstly, a deep adversarial active learning framework leverages abundant unlabeled data by training autoencoder and discriminator components adversarially to select the most informative unlabeled samples. Secondly, to mitigate the effects of imbalanced labeled datasets, a resampling phase is incorporated into the adversarial training loop. The proposed framework, named Resampling Supported Deep Adversarial Active Learning (RS-DAAL), is rigorously evaluated using two different datasets comprising social media posts from Twitter and Reddit. Various resampling techniques, including undersampling, oversampling, and hybrid methods, are assessed, with oversampling techniques further tested at different levels of resampling. Comparative studies are conducted against a baseline model without any resampling layer and with current state-of-the-art methods as well. Experimental results and statistical analysis demonstrate the superiority of the proposed RS-DAAL method in identifying COVID-19 sentiments on social media platforms. [ABSTRACT FROM AUTHOR]

A novel Al-Cu-Ni composite, featuring Ni-Al3Ni2/Al3Ni core–shell intermetallics within its matrix produced via powder metallurgy, was subjected to isothermal compression tests at different conditions so as to garner an insight into its hot deformation behaviour. These experiments were conducted from 300 to 500 °C, with strain rates varying from 0.0001 s−1 to 0.1 s−1. It is found that the flow stress is influenced by both strain rate and temperature. As the deformation temperature reduced or the strain rates increased, the peak stress level increased. To characterize the flow stress of the composite, a sine-hyperbolic constitutive equation was developed and the calculated value of activation energy (234 kJ/mol) was substantially higher than that for the self-diffusion of pure Al. Subsequently, the strain-compensated Arrhenius model and the artificial neural network (ANN) model were developed to predict the composite flow behaviour. With a lower average absolute relative error of 2.266%, a smaller root means square error of 0.668 MPa, and a higher correlation coefficient of 0.999 than the Strain-compensated Arrhenius model, the ANN model is more predictable. The processing maps show that the ideal hot working conditions for the composite are between 400 and 500 °C, with strain rates between 0.0001 s−1 and 0.01 s−1 with an efficiency of 37%. The uneven distribution in the shape and size of Core–shell/Al3Ni intermetallic compounds influenced the flow stress curves, leading to dynamic recrystallization (DRX), followed by partial dynamic recovery (DRV), and ultimately strain hardening at 0.01 s−1 & 0.1 s−1. The occurrence of recrystallization and recovery up to a specific strain followed by strain hardening at all the deformation temperatures is a rare phenomenon and has been attributed to the structure and composition of core–shell intermetallics. The occurrence of work hardening at higher strains throughout the operational range of temperatures imparts greater reliability to structural components. [ABSTRACT FROM AUTHOR]

A very crucial branch of Natural Language Processing is Sentiment Analysis, which seeks to elicit feelings in the public from feedback provided by users. This study proposes a Hybridized Deep Neural Network-based framework for the sentiment analysis, where we have modified Dispersive Flies Optimization by adjusting its neighbor counterpart and applied that Neighbour Adjusted Dispersive Flies optimization for optimizing feature space with the aid of sentiment information extracted using our specially developed SentiWordNet lexicon-linked fitness function, after preliminary processing of data. This modification helps to avoid the local optimal solution and supports the optimization process to approach the global optimal solution in more effective way. Next, to handle the textual features efficiently through Deep Learning approaches, we use pre-trained embedding technique to represent them mathematically. The Hybridized Deep Neural Network, which is made up of a Convolutional Neural Network and Long Short Term Memory, is then given the embedded features. In order to store locally implanted information, Convolutional Neural Networks construct hierarchical representations, while Long Short Term Memory attempts to recollect pertinent prior data for opinion categorization. This hybridization helps to take advantage of both the component networks. The deep neural network system ultimately delivers the desired sentiment category. To demonstrate its effectiveness, the suggested hybrid methodology is reckoned and contrasted with numerous cutting-edge methodologies utilizing a variety of performance indicators. Our proposed framework gives the best performance compared to the baselines with an accuracy of 89.0%, 81.9%, 67.9%, 64.6%, 83.2%, 79.8% and 91.3% for Amazon, ETSY, Big Basket, Facebook, Finance, Twitter and Wine dataset respectively. [ABSTRACT FROM AUTHOR]

Groundwater has been the largest accessible freshwater source globally. The overall dimensions of every basin's water budgeting are closely linked to the quantity quality of available surface and subsurface water. In this paper, the groundwater levels trend in six districts within the Baitarani River Basin, Odisha State, India has been analyzed. Data on the depth to groundwater level for pre and post-monsoon season from 32 wells spread over six different regions from 1997 to 2020 were analyzed. Sen's slope techniques and creative trend analysis were used to investigate the behavior of groundwater level changes in pre- and post-monsoon periods to identify both monotonic and non-monotonic behaviors. The findings showed that, throughout the pre-monsoon and post-monsoon periods, the depth to groundwater level for four Tubewell, five Borewell, and seven Dugwell locations increased. However, only three tubewell stations Champua, Bhadrak, and Karanjia exhibited declining trends in the pre- and post-monsoon periods. In the case of borewell, only one station (Anadapur) has a negative trend in post-monsoon. For Dugwell, during pre-monsoon Banspal represents the –ve trend, and Bhandaripokhari, Banspal, Anandapur, and Joda show negative trends in post-monsoon seasons. A significant decline in groundwater level was observed in three stations for tubewell, one station for borewell, and four stations for dugwell observed in the current study. Accordingly, spatial maps of increasing and decreasing trends of groundwater levels were prepared, which can be used by water resource managers to identify the causes of declining groundwater levels for managing the resources effectively. [ABSTRACT FROM AUTHOR]

Due to the scarcity of water supplies, coastal groundwater quality most importantly influences sustainable development in the coastal region. Rising groundwater pollution through heavy metal contamination is an intense health hazard and environmental concern worldwide. This study shows that 27%, 32%, and 10% of the total area come under the categories very high, high, and very low human health hazard index (HHHI) accordingly. This area's water quality is also much polluted; the study shows approximately 1% has very good water quality. High concentrations of Fe, As, TDS, Mg2+, Na, and Cl− are relatively noticed in the portion of the western part of this district. The concentration of heavy metals in coastal aquifers influences the groundwater pollution of that region. The average heavy metal concentration in this region is 0.20 mg/l (As) and 1.160 mg/l (TDS). The groundwater quality and hydrogeochemical properties are determined through the Piper diagram. The study stated that TDS, Cl− (mg/l), and Na+ (mg/l) are the most regulatory issues of vulnerability. In the present study region, a huge number of alkaline substances are present resulting in the water being unfit for drinking purposes. Lastly, it is clear from the study's findings that multiple risks exist there like As, TDS, Cl−, and other hydrochemical parameters in the groundwater. The proposed approach applied in this research work may be a pivotal tool for predicting groundwater vulnerability in other regions. [ABSTRACT FROM AUTHOR]

In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl−, the value of which is 0.71. Other factors like—As, F−, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies. [ABSTRACT FROM AUTHOR]

Recent studies have established the success of deep learning models in facial emotion recognition. However, such models are often not well suited to tackle one of the most commonly encountered problems of imbalanced classes. In real datasets, various emotion classes are found to be highly underrepresented leading to dramatic reduction in the performance of classification models. In the current study, a residual variational autoencoder-based model has been proposed to address the problem of imbalanced facial emotion recognition. Firstly, in order to capture the most important features in the form of embeddings, a variational autoencoder equipped with residual connections has been trained in an unsupervised fashion to obtain the most effective latent space representation of all input images. After the training phase, only the encoder part of the actual autoencoder is used to transform all labeled facial images into a latent vector form. Next, the imbalanced latent vectors are resampled using well-known algorithms to tackle the imbalanced classes. In this context, three major types of algorithms viz., Undersampling, Oversampling, and Hybrid are used for the same. To establish the quality of the proposed method, various well-known classifiers are trained and tested in terms of test phase confusion matrix-based performance indicators. All hyperparameters are selected by employing the Grid search method. In addition, to understand the effect of oversampling minority class samples, a separate study is conducted by observing classifier performance against varying degrees of oversampling. Experimental results and extensive comparative studies have shown that the residual variational autoencoder model combined with SMOTE-ENN hybrid resampling technique can boost the classifier performance to a greater extent. [ABSTRACT FROM AUTHOR]

Arsenic (As) is a toxic environmental contaminant with global public health concern. In aquatic ecosystems, the quantification of total As is restricted chiefly to the individual organisms. The present study has quantified the total As in different trophic layers (sediment–water-phytoplankton-periphyton-zooplankton-fish-gastropod-hydrophytes) of lentic freshwater ecosystems. As transfer pathways quantifying the transmission rate across trophic-level compartmental route were delineated using a novel model-based approach along with its potential contamination risk to humans. Lentic water bodies from Indo-Gangetic region, a core area of groundwater As, were selected for the present investigation. The study revealed that among the lower biota, zooplankton were the highest accumulator of total As (5554–11,564 µg kg−1) with magnification (rate = 1.129) of the metalloid, followed by phytoplankton (2579–6865 µg kg−1) and periphytic biofilm (1075 to 4382 µg kg −1). Muscle tissue of zooplanktivore Labeo catla is found to store higher As (80–115 µg kg−1 w.w.) compared to bottom-dwelling omnivore Cirrhinus mrigala (58–92 µg kg−1 w.w.). Whereas, Amblypharyngodon mola has accumulated higher As (203–319 µg kg−1 w.w.) than Puntius sophore (30–98 µg kg−1 w.w.) that raised further concern. The hepatic concentration indicated arsenic-mediated stress based on As stress index (threshold value = 1). Mrigal and Mola showed significant biomagnification among fishes while biodiminution was observed in Catla, Bata, Rohu and Punti. All the studied fishes were under the arsenic mediated stress. In the 'sediment–water-periphytic biofilm-gastropod' compartment, the direct grazing accumulation was higher (rate = 0.618) than the indirect path (rate = 0.587). Stems of edible freshwater macrophytes accumulated lesser As (32–190 µg kg−1 d.w.) than roots (292–946 µg kg−1 d.w.) and leaves (62–231 µg kg−1 d.w.). The target cancer risk (TCR) revealed a greater concern for adults consuming edible macrophyte regularly. Similarly, the varied level of target hazard quotient and TCR for adults consuming fishes from these waterbodies further speculated significant health concerns. The trophic transfer rate of environmental As in soil–water-biota level at an increasing trophic guild and consumer risk analysis have been unravelled for the first time in the Indo-Gangetic plains, which will be helpful for the strategic mitigation of As contamination. [ABSTRACT FROM AUTHOR]

Groundwater plays an important role in our environment. In recent decades, groundwater-related issues within the river basin are significantly increasing in most part of the world. It is essential to estimate the magnitude of groundwater storage changes as well as runoff generated within the river basin. The principal objectives of the study are to quantify the water budget parameters for Baitarani River Basin of India and to analyze the season-wise trend analysis of groundwater storage by using Innovative Trend Analysis (ITA). Climatic data from eight Indian meteorological stations were used to estimate direct runoff from enhanced SCS-CN (Soil Conservation Service Curve Number) method, Potential Evapo-Transpiration (PET) was estimated by using Hargreaves method in R studio platform. The analysis showed that precipitation and surface runoff followed a non-monotonic negative (− ve) trend in winter season and monotonic positive (+ ve) trend in all other season for all stations. It was also observed that the groundwater storage is increasing in winter and post-monsoon season. In the lower catchment areas, groundwater storage was more due to flat topography and good aquifer type, where groundwater use can be enhanced. But in upper catchment areas, groundwater storage capacity is comparatively less hence rain water conservation structures could be suggested to check the surface runoff and provide options for increasing groundwater recharge. This study will be helpful to focus on regional scale planning to address the water management issues within the basin, which will be ultimately helpful for agriculture, watershed development and environmental planning. [ABSTRACT FROM AUTHOR]

In practical quantum error correction implementations, the measurement of syndrome information is an unreliable step—typically modeled as a binary measurement outcome flipped with some probability. However, the measured syndrome is in fact a discretized value of the continuous voltage or current values obtained in the physical implementation of the syndrome extraction. In this paper, we use this "soft" or analog information to benefit iterative decoders for decoding quantum low-density parity-check (QLDPC) codes. Syndrome-based iterative belief propagation decoders are modified to utilize the soft syndrome to correct both data and syndrome errors simultaneously. We demonstrate the advantages of the proposed scheme not only in terms of comparison of thresholds and logical error rates for quasi-cyclic lifted-product QLDPC code families but also with faster convergence of iterative decoders. Additionally, we derive hardware (FPGA) architectures of these soft syndrome decoders and obtain similar performance in terms of error correction to the ideal models even with reduced precision in the soft information. The total latency of the hardware architectures is about 600 ns (for the QLDPC codes considered) in a 20 nm CMOS process FPGA device, and the area overhead is almost constant—less than 50% compared to min-sum decoders with noisy syndromes. [ABSTRACT FROM AUTHOR]

Sentiment Analysis is a highly crucial subfield in Natural Language Processing that attempts to extract the public sentiment from the accessible user opinions. This paper proposes a hybridized neural network based sentiment analysis framework using a modified term frequency-inverse document frequency approach. After preprocessing of data, the basic term frequency-inverse document frequency scheme is improved by introducing a non-linear global weighting factor. This improved scheme is combined with the k-best selection method to vectorize textual features. Next, the pre-trained embedding technique is employed for the mathematical representation of the textual features to process them efficiently by the Deep Learning methodologies. The embedded features are then passed to the deep neural network, consisting of Convolutional Neural Network and Long Short Term Memory. Convolutional Neural Networks can build hierarchical representations for capturing locally embedded features within the feature space, and Long Short Term Memory tries to recall useful historical information for sentiment polarization. This deep neural network finally provides the sentiment label. The proposed model is compared with different state-of-the-art baseline models in terms of various performance metrics using several datasets to demonstrate its efficacy. [ABSTRACT FROM AUTHOR]

Recent research works have established the importance of sarcasm detection in the domain of sentiment analysis. Automatic sarcasm detection using social media data is a challenging task in the presence of imbalanced classes. Real-life social media data often suffer from this problem of class imbalance resulting in dramatical degradation of the performance of classification models attempting to detect sarcasm. Motivated by this, in the current article, a Bi-LSTM variational autoencoder model has been proposed to alleviate the problem of imbalanced classes in social media datasets targeted to train sarcasm detection models. The proposed BVA model is trained with a large corpus of sarcastic and non-sarcastic tweets to obtain the most suitable latent space representation of the same. These inherently class-biased latent vectors are then oversampled using synthetic minority oversampling techniques. The quality of the proposed method is established by training and testing a set of well-known classifiers in terms of precision, recall, F1-score, AUC, and G-mean. Extensive experiments reveal that the proposed BVA model combined with oversampling techniques can improve classifier performance for sarcasm detection to a greater extent. [ABSTRACT FROM AUTHOR]

Video summarization is the process of refining the original video into a more concise form without losing valuable information. Both efficient storage and extraction of valuable information from a video are the challenging tasks in video analysis. Intelligent video surveillance system has an essential role for ensuring safety and security to the public. Recent intelligent technologies are extensively using the surveillance systems in all areas starting from border security application to street monitoring systems. Now the surveillance camera or motion sensitivity-based cameras produce large volume of data when employed for recording videos. As analysis of videos by humans demands immense manpower, automatic video summarization is an important and growing research topic. Hence, it is necessary to summarize the activities in the scene and eliminate unusual and redundant events recorded in videos. The proposed work has developed a video summarization framework using key moment-based frame selection and clustering of frames to identify only informative frames. The key moment is a simple yet effective characteristic for summarizing a long video shot and motion is the most salient feature in presenting actions or events in video which is used here to extract the key moments of the video frames. The motion is the scene of a video frame which has the most acceleration and deceleration in case of the key moments. Based on the extracted key moments, the frames of the video are partitioned into different groups using a novel similarity-based agglomerative clustering algorithm. The algorithm determines at most K clusters of frames based on Jaccard similarity among the clusters, where K is the user defined parameter set as the 5% to 15% of the size of the video to be summarized. From each cluster, few representative frames are identified based on the centroids of the clusters and arranged according to their original video sequence to generate the summary of the video. The proposed clustering algorithm and the summarization method are evaluated using state-of-the-art video datasets and compared with some related methodologies to demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised Variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., "COVID-19", "Pneumonia", and "Normal". In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance. [ABSTRACT FROM AUTHOR]

Ni-TiO2 catalysts have been successfully developed by simple sol–gel techniques with variations in Ni2+ concentration. The reduced bandgap suggests the appearance of the intragap various localized defect states and oxygen vacancies. The formation of oxygen vacancies and its effect on photocatalysis with the presence of the colour centres (F, F+, F++) in the photocatalytic mechanism by replacement of anion vacancy and electron pair displacement are discussed. The low concentration (0.02 M) of Ni2+ doping represents a large amount of oxygen vacancy, which assures the high capability of visible light absorbance. The chemical reaction mechanism of oxidation/hydrogenation-induced photocatalytic behaviour through formation of Leuco-MB is also established. The design of anatase/rutile heterostructure and the proposed mechanism of Schottky induced charge transfer phenomenon under visible light irradiation are also embedded in our work. In comparison with many other early reports, our results show that the 0.02 M concentration of Ni2+ doping has an outstanding photocatalytic activity with complete decolouration with evolution of Leuco-methylene blue and thus obtained 100% total degradation of toxic MB compounds from the aqueous solution after just only 30 min of visible light illumination. [ABSTRACT FROM AUTHOR]

Nanosizing antiparasitic formulation has tremendous potential to control parasite infection by rectifying drawbacks of conventional formulations. The present study investigates synthesis optimization of cypermethrin nanoemulsion using PEG diblock copolymers through a spontaneous emulsification technique. Synthesis parameters of nanoemulsion were optimized using response surface methodology with central composite design to get nanoemulsion (NEC-1) with lowest particle size (179.8 nm) and highest encapsulation efficiency (90.24%). TEM and DLS revealed the spherical morphology and negative zeta potential (− 42.4–57.2 mV) of the developed nanoemulsion formulations. Bioefficacy of the NEC-1 was studied against fish ectoparasite Argulus bengalensis at embryonic and adult stages. It was observed that treatment of nanoemulsion disrupted the natural developmental stage of A. bengalensis egg at a very low concentration (0.001 ppm). The calculated LC50 against the adult parasite and LC50 for prevention of 50% egg hatching was found to be 0.005 and 0.006 ppm, respectively. In the recent scenario of repetitive antiparasitic resistance, the developed nanoemulsion could come out as a potential solution; however, further study is required for its feasibility for use in actual field conditions. [ABSTRACT FROM AUTHOR]

The article titled "A rough set theory and deep learning-based predictive system for gender recognition using audio speech" has been retracted by the publisher due to concerns about compromised editorial handling and peer review processes. The investigation found issues with inappropriate references and the article not being within the scope of the journal or guest-edited issue. The authors of the article disagree with the retraction. [Extracted from the article]

The present study explored and assessed the spatio-temporal variations in diversity, abundance, composition of macrozoobenthic community, and their relationship with environmental parameters in a tropical small reservoir, India. Periodical samples of macrobenthic community and environmental variables were collected from four sampling stations covering three horizontal transacts in each station during 2017 to 2019. Altogether, a total of 16 macrozoobenthic species were recorded during the study period under four Phyla: Gastropoda (6), Insecta (4), Oligochaeta (4), and Bivalvia (2). A total of 12 taxa were recorded at station 1 (S1) and station 2 (S2) spatially whereas 16 taxa were recorded during monsoon season temporally. The study showed that Oligochaeta (79%) forms the most dominating group followed by Insecta (17%), Gastropoda (3%), and Bivalvia (1%). The species like Limnodrilus hoffmeisteri, Tubifex tubifex, and Branchiura sowerbyi were the most dominating species encountered in all the sampling stations and seasons. Spatially, the macrozoobenthos density was varied from 535 ± 123 to 1449 ± 289 ind./m2, being maximum at S2 (transition/limnetic zone). The maximum value for diversity indices (Shannon-diversity and Margalef's indices) were recorded at station 1 (riverine zone) during monsoon season. The water quality like total alkalinity, air temperature, and water temperature were the deterministic factors for the distribution, community structure, and abundance of macrozoobenthic fauna. The study indicated that macrozoobenthic community support directly as a fish food organism to about 28.57% fishes recorded from this reservoir. The study generated baseline information on the macrozoobenthic community pattern of reservoir in relation to water quality. Based on the study, ecosystem-based fisheries management strategies could be developed for conservation and fisheries enhancement of small tropical reservoirs. [ABSTRACT FROM AUTHOR]

Anatase titania thin films were prepared by hydrothermal assisted sol-gel dip coating at two different hydrothermal temperatures: 90 °C and 180 °C for 12 h each. Some of the as-deposited films were annealed at 500 °C for 6 h. Both as-deposited and annealed films consisted of tiny spherical particles. Crystallite size and particle size increased with increased hydrothermal temperature and annealing. Atomic force microscopy showed that root mean square and average surface roughness increased with increased hydrothermal temperature and annealing. The prepared films exhibited almost zero transmittance in the violet-ultraviolet transition region with a gradual rise in the visible region up to a maximum value of ~40% at the near infrared. The transmission decreased for annealed films and as hydrothermal temperature was increased. Band gap values did not show any significant difference before and after annealing, although they decreased with increased hydrothermal temperature. Improved crystallinity and greater packing density at higher hydrothermal temperature and annealing led to a corresponding increase in the refractive index. The intensity of photoluminescence peaks was quenched when samples were annealed and as the hydrothermal temperature increased, because of annihilation of oxygen vacant states by the ambient oxygen. Improved crystallinity diminished the number of defect sites in the films, thus reducing the amount of radiative recombination of the e‒/h+ pair. Annealed samples and those prepared from sols processed at higher hydrothermal temperature showed better photocatalytic activity. The maximum degradation efficiency of 62.8% was demonstrated by annealed thin films prepared from sols hydrothermally processed at180 °C after 90 minutes of UV irradiation. Highlights: Anatase TiO2 was obtained in an autoclave at low temperature (90 °C). The phase remained unaltered after annealing at 500 °C and also at elevated hydrothermal synthesis temperature of 180 °C. Agglomerated spherical particles were produced with Gaussian type size distribution. Higher preparation temperature and annealing quenched the photoluminescence (PL) intensity that eventually increased efficiency of photo-degradation process. Efficient photodegradation was achieved only after 90 minutes of UV irradiation with a very small area thin film photocatalyst (1.5 cm ⨯ 1 cm). [ABSTRACT FROM AUTHOR]

The structural and optical properties of polypyrrole-ZnO (PPy-ZnO) composites with different amount of ZnO are investigated. The nature of optical absorption and emission of the composites having agglomerated granular morphology is explored. Addition of ZnO slightly affects the conjugation of the polymer chain due to which the π-π* transition of PPy-ZnO composite shows some blue shift in the UV-Vis absorption spectra. The photoluminescence intensity of PPy-ZnO nanocomposite varies with variation of ZnO content. This tunability in the photoluminescence intensity of PPy-ZnO nanocomposite can be understood in terms of Forster Resonance Energy transfer (FRET) from ZnO (donor) to PPy (acceptor). The energy transfer efficiency, Forster radius and separation between donor and acceptor are predicted theoretically by using a FRET model. With the change in donor concentration, the energy transfer efficiency changes which affects the emission intensity of composites. The highest emission intensity is observed for 1.22 g of ZnO which indicates the highest energy transfer between ZnO and PPy. Thus, by tuning ZnO content the emission properties of the PPy-ZnO composite can be tuned. Highlights: Semiconducting PPy-ZnO composites are synthesised here with different amount of ZnO. Enhanced photoluminescence is obtained in composite as compared to pristine PPy. FRET from ZnO to PPy is responsible for the PL enhancement. Blue emission from all the composites is observed. [ABSTRACT FROM AUTHOR]

H+ and Al3+ co-doped Polyaniline thin films have been synthesized by electropolymerization method with the variation of aluminium nitrate salt (Al2(NO3)3, 9H2O) into the electrolyte. FESEM, atomic force microscopy and UV–Vis-NIR spectrophotometry were used to characterize the nanostructured polyaniline thin films. FESEM showed nanowire like structures of the thin films. AFM study indicated an enhancement of surface roughness with increase in Al3+ concentration. Enhancement in photoluminescent emission was also observed with the increase of Al3+ amount into the electrolyte solutions. Supercapacitive behaviour of the films was investigated using cyclic voltammetry; areal capacitance showed an increasing trend with increase of Al3+ concentration. Electropolymerized PAni doped with Al3+ can be used as electrode materials for supercapacitor application. [ABSTRACT FROM AUTHOR]

A series of nanomaterials of ZnO have been synthesized via chemical co-precipitation method with fixed proportions of precursors and varied solvents. X-ray diffraction confirms wurtzite crystalline structure with nanometric crystallites (< 23 nm). Texture coefficient of crystallographic orientations show remarkable change for switching the solvents from water to alcohol. Morphological study reveals nanomaterials resembling prolate, sphere and oblate shaped structures for the solvents water, methanol and ethanol, respectively, with increasing particle size. All the nanomaterials show a similar absorption band in the UV region; though, more absorption covering a wider region in visible range is observed for nanomaterials prepared in alcoholic solutions. Red shifting in band gap of nanomaterials is correlated with band-tail effect. Variation in Urbach energy indicates that the nature of solvent plays a vital role in creating defects in ZnO, justifying enhanced absorptions in visible region. Photoluminescence (PL) spectra show various emission bands consisting of blue, green and yellow emissions corresponding to different intrinsic defects in nanomaterials. PL displays a tuning trend for blue–green emission by changing solvent from water to alcohol. However, overall enhanced PL intensity and particularly intense blue emission have been achieved by replacing water with alcohol. Tunability in emission colours and high colour purity is observed in the CIE chromaticity analysis. Theoretically, estimated electroluminescence of ZnO prepared in alcohol shows superiority compared to ZnO prepared in water. The mechanism of solvent-mediated defect creation and emission in ZnO will be beneficial for future QD LED applications. [ABSTRACT FROM AUTHOR]

Luminescent polypyrrole–polymethylmethacrylate (PPy–PMMA) blends have been obtained by bulk polymerization of MMA in presence of different concentration of PPy nanowires. The PPy nanowires are distributed over the rock-like features of PMMA as observed from SEM images. Raman spectra indicate the formation of blend. PPy–PMMA blends exhibit high photoluminescence emission intensity though PPy is not a fluorescent polymer. All the blends exhibit blue emission as observed in CIE diagram. Enhanced emission from the blend as compared to pristine PPy is obtained due to Forster resonance energy transfer (FRET) between PMMA and PPy. The emission intensity of the blend becomes highest for an optimized amount of both the components, i.e. 0.8 g of PPy to 1 ml of MMA in the blend with energy transfer efficiency of 66% and high colour purity. So, optimization of the amount of PPy and PMMA in the blend is important to get high luminescence from the PPy–PMMA blend. The maximum electroluminescence quantum efficiency of the polymer light-emitting diode (PLED) using optimized PPy–PMMA blend as active layer is found to be 2% at turn on voltage of 3 V for the emission at wavelength of 400 nm, whereas at turn on voltage of 3.5 V, the PLED emits light of wavelength 330 nm. [ABSTRACT FROM AUTHOR]

A series of ZnO thin films has been synthesized using an electrodeposition system on Indium tin oxide (ITO) coated glass substrate by varying zinc nitrate concentration. For the synthesized thin films, the composition, morphology, crystalline structure, and optical properties are characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), grazing incidence X-ray diffraction spectroscopy (GIXRD), UV–Vis absorption spectroscopy, and photoluminescence spectroscopy (PL). The formation of Zn–O bonds and the increment of nitrate and –OH groups with increasing zinc nitrate concentration are revealed by FTIR spectroscopy. The GIXRD results show a slight shift towards the higher 2θ angle for all ZnO-related diffraction peaks, confirming the presence of micro-strain in all electrodeposited thin films. The estimated strain values increase with increasing precursor concentration, justifying the increment in crystal defects. SEM images exhibit an evolution of surface morphologies from nanosphere-like to nanosheet or nanoleaf-like structures with the zinc nitrate concentration increment. UV–Vis spectra reveal a red shift in band edge absorption from the lowest to highest zinc nitrate concentration sample; however, the edge again exhibits a blue shift for the moderate zinc nitrate concentration thin film. The calculated bandgap values for all samples are lie within the range of 3·07–2·81 eV. The PL spectra declare an enhancement in emission intensity with increasing precursor concentration. This variation in emission intensity is explained by the change in nanostructures that eventually tuned the defect states and subsequently the luminescence. A UV-light-driven photocatalytic degradation of MO dye in water has been studied to assess the photocatalytic activities of ZnO thin films using UV–Vis absorption spectroscopy. The results show the best photocatalytic efficiency (70.43%) for moderate precursor concentration thin film, achieved by an optimum concentration of surface oxygen vacancy states. However, due to a huge surface oxygen vacancy, the highest precursor concentration thin film exhibits the minimum catalytic efficiency (32.36%). It declares that an excessive amount of surface oxygen vacancy can create bulk oxygen vacancies, which eventually ruins the catalytic activity and reduces the photocatalytic performance. [ABSTRACT FROM AUTHOR]

TiO2 photocatalysts were synthesized by facile sol–gel process with compensation of cetyltrimethylammonium bromide (CTAB). We report the influence of CTAB addition on morphological, electronic properties, stoichiometric and photocatalytic activity of anatase-enriched CTAB–TiO2. Here CTAB acts as a good surface modifier. Observed quenched photoluminescence (PL) intensity signifies high charge separation rate and reduced recombination probability of charge carriers. CTAB takes part in trapping electrons, which hinders the recombination of electron–hole pairs. X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy analyses show the formation of oxygen vacancies and Ti3+-defect states. These defect states are also confirmed from PL analysis. X-ray diffraction pattern reveals the increase of crystallinity of the catalyst sample on increasing CTAB amount in the TiO2 matrix. The estimated decreasing nature of particle size of the samples claimed that agglomeration of particles was destroyed with addition of CTAB and the surface area increases accordingly. The broadening of XRD patterns for higher CTAB-assisted samples was attributed to the formation of point defects. Here, results show that the bridging oxygen vacancies and other defect states played a crucial role by interacting with water molecules and dissociation of the Ti-O bonds to form titanium ions. Photocatalytic activity of methylene blue degradation in UV light by varying catalyst loading was investigated. Kinetic modelling of degradation has been established in this study. Photodegradation of dye increases at higher amounts of catalysts due to the presence of more OH. radicals in the active sites. [ABSTRACT FROM AUTHOR]

In this article, we have delved into detecting dense regions of weighted sparse networks through identification of cliques and communities. A novel clique finding method is introduced to generate all maximum cliques of a sparse network, with focus on analysis of real-life networks and a community detection algorithm is devised on maximal cliques to determine possible overlapping communities of the weighted sparse network. A good number of methods of clique detection already exist, some of which are truly efficient, but many of them lack direct applicability to real-life network analysis, as they deal with simple networks, hide intermediary details and allow cliques to be formed without information on strength of interactions in group behavior. The proposed method attaches a clique-intensity value with every clique and using two thresholds on intensity of interactions, at the individual level and group level, provides handle to filter stray or insignificant interactions at various stages of clique formation. Using differently sized weighted maximal cliques as building blocks, a new overlapping community detection method is proposed using a new measure, a weighted version of Jaccard index, called weighted Jaccard index. Experiments are done on real-life networks; the maximum clique structures reveal sensitivity to threshold values, while the resulting community structures demonstrate efficacy of the community detection method. [ABSTRACT FROM AUTHOR]

The non-invasive treatment strategy is indispensable to overcome the side effects of conventional treatment with chelating agents against arsenic. Presence of catechins and flavonoids in Camellia sinensis have potential antioxidant properties and other beneficial effects. The aim of the study was to explore the curative potential role of Camellia sinensis against uterine damages produced by sodium arsenite in mature albino rats. A dose of 10 mg of Camellia sinensis ethyl acetate (CS-EA) fraction/100 gm body weight was provided to the sodium arsenite-treated rats (10 mg/Kg body weight). LC-MS analysis was used for the detection of active component in CS-EA fraction. Enzymatic antioxidants analysis carried out by reproducible native gel technique. Hormones and some pro and anti-inflammatory markers were detected by ELISA, PCR, and western blot techniques respectively. Immunostaining was performed for the detection of estradiol receptor alpha. LC-MS analysis of CS-EA fraction ensured the presence of active tea polyphenol and tea catechin of which highest peak of epigallocatechin-3 gallate (EGCG) was obtained in this study. Significant elevations of lipid peroxidation end products followed by the diminution of antioxidant enzymes activities were noted in arsenicated rats which were capably retrieved by the treatment of CS-EA fraction. Post-treatment with CS-EA fraction meaningfully improved gonadotrophins and estradiol signalling in association with a highly expressing estradiol receptor-α (ERα) in the ovary and uterus followed by the maintenance of normal utero-ovarian histoarchitecture in arsenic fed rats. CS-EA fractioned treated group overturned the sodium arsenite driven higher expression of pro-inflammatory cytokines and proapoptotic markers along with a low level of anti apoptotic Bcl-2 expression and comparatively lower NF-κB signalling in the uterus via regulating IKK β kinase mostly by EGCG of CS-EA fraction. However, ethyl acetate fraction of Camellia sinensis played a critical role in minimizing arsenic-mediated uterine hypo-function. [ABSTRACT FROM AUTHOR]

The antioxidant and antimicrobial activity of the essential oil extracted from Syzygium aromaticum was investigated. Extraction of oil was carried out following the standard methodology of supercritical CO2 extraction. Antiradical and antimicrobial activity of extracted oil was studied. Chemical constituent of oil exhibited 65.3% eugenol. It was found that the extracted clove essential oil demonstrated high antioxidant contents which lead to free radical scavenging, reducing power, metal chelation and inhibition of lipid peroxidation. Shigella dysenteriae and Staphylococcus aureus showed susceptibility towards oil. Minimum Inhibitory concentration values for two tested bacteria are 3.0 and 2.5 mg/ml. Growth inhibition around well loaded with different concentration of oil further confirmed antimicrobial property. Additionally, clove essential oil also seen to be compatible to goat's erythrocytes. Findings suggest that clove essential oil may act as therapeutic molecules against diseases caused by pathogens and free radicals. [ABSTRACT FROM AUTHOR]

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-objective evolutionary algorithm is applied for generating the ensemble summary of the target article. Each sentence of the base summaries is represented by a concept vector of the medical terms contained in it with the help of the Unified Modelling Language System (UMLS) tool which is widely used in various smart medical applications. These terms carry the key information of the sentence which is very useful to find out the semantic similarity among the sentences. Fitness functions of the evolutionary algorithm are mainly defined using clustering coefficient and sparsity index, the concepts of graph theory. After the convergence of the algorithm, the best solution of the final population gives the ensemble summary. Next, the semantic similarity of each sentence in the target article with the ensemble summary is calculated and the sentences which are most similar to the ensemble summary are considered as the summary of the target article. The method is applied to the articles available in the PubMed MEDLINE database system and experimental results are compared with some state of the art methods applied in the Bio-medical domain. Experimental results and comparative study based on the performance evaluation show that the method competes with some recently proposed summarization methods and outperforms others, which express the effectiveness of the proposed methodology. Different statistical tests have also been made to observe that the method is statistically significant. [ABSTRACT FROM AUTHOR]

Okra production in eastern India at present is severely threatened by whitefly-mediated okra enation leaf curl disease (OELCuD). Identification of resistant genotype and understanding the genetic control and biochemical relationship of OELCuD resistance are prerequisite for developing an effective breeding strategy. This study was conducted employing six populations (P1, P2, F1, F2, BC1 and BC2) of two selected (resistant × susceptible (R×S)) crosses. Associationship between severity of OELCuD and biochemical parameters of parents and hybrids at preflowering and flowering stages was studied. Segregation pattern of the genotypes in F2 generation showing OELCuD reaction of two crosses suggested that two duplicate recessive genes was operative for resistance to OELCuD. Generation mean analysis revealed involvement of both additive and nonadditive effects in the inheritance of disease resistance. Hence, postponement of selection in later generations or intermating among the selected segregates followed by one or two generations of selfing to break the undesirable linkage and allow the accumulation of favourable alleles could be suggested for the development of stable resistant genotype against this disease. Higher peroxidase activity and total phenol content in leaf emerged as reliable biochemical markers for early selection of genotype resistant to OELCuD. [ABSTRACT FROM AUTHOR]

The present study focuses on maintaining the temperature magnitude around heat-sensitive components (cloaked region) in advanced electronic devices by introducing convective elements using extended surface fins. A finite element analysis confirmed that with the aid of the convection component to thermal cloaking, heat flux can be redirected around the cloaked region as well as control the temperature. The simulation results were verified by experiment under natural convection corresponding to the simulation assumptions. It was found that when the heat source maintains its temperature at 100 °C and the heat sink remains at 0 °C, the temperature within the cloaked region can reduce by up to 15 °C, from ~ 50 °C with conventional cloaking to 35 °C with a well-designed array of surface fins. It is worth noting that experimental results are consistent with the simulation results. [ABSTRACT FROM AUTHOR]

Many crime reports are available online in various blogs and Newswire. Though manual annotation of these massive reports is quite tedious for crime data analysis, it gives an overall crime scenario of all over the world. This motivates us to propose a framework for crime data analysis based on the online reports. Initially, the method extracts the crime reports and identifies named entities. The intermediate sequence of context words between every consecutive pair of named entities is termed as a crime vector that provides relationships between the entities. The feature vectors for each entity pair are generated from these crime vectors using the Word2Vec model. The paper considers three different types of named entity pairs to facilitate the major crime data analysis task, and for each type, similarity between every pair of entities is measured using respective feature vectors. For each type of named entity pair, a separate weighted graph is generated with entity pairs as vertices and similarity score between them as the weight of the corresponding edge. Then, Infomap, a graph-based clustering algorithm, is applied to obtain optimal set of clusters of entity pairs and a representative entity pair of each cluster. Each cluster is labelled by the relationship, represented by the crime vector, of its representative entity pair. In reality, all the entity pairs in a cluster may not reflect contextual similarity with their representative entity pair. So the clusters are further partitioned into subclusters based on WordNet-based path similarity measure which makes the entity pairs in each subcluster more contextually similar compared to their original cluster. These subclusters provide us various statistical crime information over the time period. The method is experimented only using the crime reports related to crime against women in India. The experimental results demonstrate the effectiveness and superiority of the method compared to others for analysing the crime data. [ABSTRACT FROM AUTHOR]

Nowadays, crime is a major threat to the society that affects the normal life of human beings all over the world. It is very important to make the world free from all aspects of crime activities. The main motivation of this work is to understand various crime patterns for avoiding and preventing the crime events to occur in future and save the world from such curse. Though research is going on for solving such problems, no work is noticed to handle the roughness or ambiguity that exists in the crime reports. The present work extracts all possible crime features from the crime reports and selects only the important features required for crime pattern analysis. For this purpose, it develops a purely supervised feature selection model integrating rough set theory and graph theory (spanning tree of a directed weighted graph). The crime reports are preprocessed, and crime features are extracted to represent each report as a feature vector (i.e., a set of distinct crime features). For crime pattern analysis, the main objective of our work, all extracted features are not necessarily essential, rather a minimal subset of relevant features are sufficient. Thus, feature selection is the main contribution in the paper that not only enhances the efficiency of subsequent mining process but also increases its correctness. The rough set theory-based relative indiscernibility relation is defined to measure the similarity between two features relative to the crime type. Based on the similarity score, a weighted and directed graph has been constructed that comprises the features as nodes and the inverse of the similarity score representing the similarity of feature v to u as the weight of the corresponding edge. Then, a minimal spanning tree (termed as rough-spanning tree) is generated using Edmond/Chu–Liu algorithm from the constructed directed graph and the importance of the nodes in the spanning tree is measured using the weights of the edges and the degrees (in-degrees and out-degrees) of the nodes in the spanning tree. Finally, a feature selection algorithm has been proposed that selects the most important node and remove it from the spanning tree iteratively until the modified graph (not necessarily a tree) becomes a null graph. The selected nodes are considered as the important feature subset sufficient for crime pattern analysis. The method is evaluated using various statistical measures and compared with related state-of-the-art methods to express its effectiveness in crime pattern analysis. The Wilcoxon rank-sum test, a popular nonparametric version of the two-sample t test, is done to express that the proposed supervised model is statistically significant. [ABSTRACT FROM AUTHOR]

In the era of digital boom, single classifier cannot perform well in various datasets. Ensemble classifier aims to bridge this performance gap by combining multiple classifiers of diverse characteristics to get better generalization. But classifier selection highly depends on the dataset, and its efficiency degrades tremendously due to the presence of irrelevant features. Feature selection aids the performance of classifier by removing those irrelevant features. Initially, we have proposed a bi-objective genetic algorithm-based feature selection method (FSBOGA), where nonlinear, uniform, hybrid cellular automata are used to generate an initial population. Objective functions are defined using lower bound approximation of rough set theory and Kullback–Leibler divergence method of information theory to select unambiguous and informative features. The replacement strategy for creation of next-generation population is based on the Pareto optimal solution with respect to both the objective functions. Next, a novel bi-objective genetic algorithm-based ensemble classification method (CCBOGA) is devised to ensemble the individual classifiers designed using obtained reduced datasets. It is observed that the constructed ensemble classifier performs better than the individual classifiers. The performances of proposed FSBOGA and CCBOGA are investigated on some popular datasets and compared with the state-of-the-art algorithms to demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

Cutting tool suffers rapidly during machining titanium-based alloys due to low thermal conductivity. Thus most of the heat is concentrated on the tool rather than chip during machining. To overcome this problem, a suitable cutting parameter, tool geometry, and sustainable methods are necessary. This paper presents the effect of MoS2 solid lubricant (SL), cutting speed, and nose radius during turning of Ti-6Al-4V alloy using the TiAlN coated carbide tool. The experiments are performed at different cutting speeds, nose radius, and flow rates of solid lubricant to study tool wear, surface roughness, and chip morphology. The results show that the use of solid lubricant reduces the tool wear (37%), and the surface roughness (65%) compared to the dry cutting. Similarly, the effects of nose radius and cutting speed have also been studied for both conditions. [ABSTRACT FROM AUTHOR]

For the first time, aluminum oxynitride (ALON) powders were synthesized using the precursors processed through aqueous sol–gel procedures. Appropriate quantities of aluminum nitride (AlN) powder and aqueous boehmite sol were mixed to obtain molecular stoichiometry of AlON. However, during the course of process, AlN powders reacted with the aqueous boehmite sol and disintegrated into its hydrated compounds. Such reactions altered the molecular stoichiometry between boehmite and AlN which was initially formulated for the eventual formation of AlON. Extensive investigations were carried out to analyse the behavior of AlN during the sol–gel processing. Hydrolysis of AlN in the aqueous sol–gel medium was circumvented by subjecting AlN to a surface modification process. The homogeneously dispersed boehmite sol and AlN mixture was further gelled, dried, and heat treated at temperatures between 1600 and 1850 °C for the formation of AlON powder. AlON phase formation was confirmed through XRD investigations, and its physical and microstructural properties were also evaluated through FESEM and TEM analyses. AlON powder with an average particle size of 490 nm was successfully synthesized in this study. This process is suitable for producing AlON powder in bulk quantities, and it can be used readily for further processes such as shaping and sintering to produce various optical products based on AlON. Highlights: For the first time, Aluminum Oxynitride (AlON) powders were synthesized using aqueous sol–gel processing. AlN powders and aqueous boehmite sol were used as precursors. Surface modification treatement was carried out for AlN powder prior to its addition with the boehmite sol to retain its phase intact during the processing. Single-phase AlON powders with average particle size of 490 nm was successfully synthesized. [ABSTRACT FROM AUTHOR]

The purpose of the study was to determine the phases of the menstrual cycle in the reproductive age group of females who committed suicide as compared with a control group of females who died from causes other than suicide. The study included 86 cases in the suicidal group and 80 cases in the non-suicidal group. The menstrual phase was decided by the gross and histological examination of the uterus and ovary at autopsy. Deaths were more common during the secretory phase (56.9%) in the suicidal group, while in the non-suicidal group, death occurred more commonly in the proliferative phase (66.3%). In reference to proliferative phase, deaths were more in the secretory phase and menstrual phase in the suicidal group, adjusted odd's ratio (OR) being 3.7 (p = 0.042) and 4.7 (p = 0.032), respectively. Corpus luteum was present in the right ovary of 43 and 14 victims of suicidal and non-suicidal deaths, respectively, while it was in the left ovary of 3 and 11 victims of suicidal and non-suicidal death, respectively. Odd's ratio was 10.3 for corpus luteum to be in the right ovary in comparison with the left ovary for the suicidal group (p = 0.001). This study revealed that suicidal chances in a woman are significantly more in the menstrual phase and the secretory phase of the menstrual cycle. The presence of corpus luteum in the right ovary is associated with an increased risk of suicide, but the reason is not known. [ABSTRACT FROM AUTHOR]

This paper describes the effect of temperature (85 °C) and relative humidity (RH) (85%) on structure and material properties of an environmental-friendly Sn–3.0Ag–0.5Cu, in wt.% (SAC305) material. A detail microstructure characterization was performed by scanning electron microscope with EDS analysis, X-ray diffraction, electron backscattered diffraction and transmission electron microscope techniques. From this structural analysis, it is evident that in the as-cast alloy, submicron-sized acicular and spherical-shaped ε-Ag3Sn and very fine η-Cu6Sn5 intermetallic compound (IMC) particles are homogeneously distributed in fine β-Sn matrix grains. However, after exposing SAC305 alloy to 85 °C and 85% RH for 60 days, the coarsening of its microstructure was observed changing significantly the morphology (size and shape) of the IMCs as well as of the Sn matrix. The Sn matrix grains size (in diameter) in as-cast alloy ranges within 20–30 µm, while after heat treatment an increase in size of 35–45 µm was observed. Furthermore, the ε-Ag3Sn IMC particles appeared with coarse structure. Therefore, the mechanical properties of SAC305 material turned inferior that confirmed by electrical resistivity and mechanical properties measurements. The electrical resistivity and microhardness values were reduced by about 11.2% and 22%, respectively, with respect to the corresponding values of the reference alloy (as-cast alloy). However, the heat-treatment method enhanced the damping properties of SAC305 material. [ABSTRACT FROM AUTHOR]

Objective: To reduce extubation failure rate by implementing protocol-driven ventilation and extubation strategies. Methods: Quality improvement project in a level II neonatal care unit from April 2017 to January 2018. Ventilation and extubation protocols implemented from 1 August, 2017. 18 ventilated newborns in the pre-protocol period, 16 in Plan-do-check-act (PDCA) cycle I and 17 in PDCA cycle II. Primary outcome was extubation failure within the first 72 h of extubation. Results: Extubation failure rate reduced from 41.7% (pre-protocol period) to 23.8% (PDCA 1 and 2, OR 0.44, 95% CI 0.12 to 1.59, P = 0.21). Median time to first extubation attempt significantly decreased (71.5 h to 38 h, P=0.046). Conclusions: A protocolized approach through quality improvement initiative demonstrated a sustained improvement in successful extubation with a significant reduction in the median time to first extubation attempt in ventilated newborns. [ABSTRACT FROM AUTHOR]

The current study investigates the absorption kinetics of carbon dioxide (CO2) released from power plant exhaust using activated mixture of 2-amino-2-methyl-1-propanol (AMP) upgraded by piperazine (PZ). An absorption experiment of (AMP+PZ+H2O) was conducted in a wetted wall column absorber with a temperature variation of 298–313 K and CO2 partial pressure of 5–15 kPa. PZ is considered to be a rate promoter by adjustable mass proportion varying from 2 to 10 wt%, while the concentration of entire amine solution remained constant at 30 wt%. Based on the Zwitterions mechanism, an overall reaction pattern of (AMP+PZ+H2O) with CO2 was designed. Considering pseudofirst order reaction criteria, the kinetic rate factors and the overall second order rate constants were calculated. The overall rate constant (kOV) experienced a significant enhancement with a small addition of PZ into aqueous AMP solution. The observed second-order rate constants (k2, PZ) in this experimental study were 60,403, 81,925, 98,591 and 116,521 m3·kmol−1·s−1 at 298, 303, 308 and 313 K correspondingly. The experimental specific rate of absorption into (AMP+PZ+H2O) in connection with the model anticipated rate was determined with deviation of around 4.86% average absolute deviation (AAD). [ABSTRACT FROM AUTHOR]

Open-cell ceramic foams are new class of materials with wide range of engineering applications due to their unique combination of thermal, structural and mechanical properties. These materials have not been well explored in the field of energy absorption applications due to their inherent brittleness in nature. In the present investigation, the alumina (Al2O3)-based ceramic foams with different pore sizes such as 10 PPI, 20 PPI, 30 PPI (pores per inch) were fabricated by the polymer sponge replication process. Attempt was made to modify the brittle nature of ceramic foams with energy absorption property by aluminium molten metal infiltration. All the fabricated samples were tested under quasistatic compression test. This study revealed that 30 PPI foam had high energy absorption property compared to all other foams, and also this energy absorption properties had been significantly enhanced through infiltration due to composite effect. [ABSTRACT FROM AUTHOR]

Structural properties evaluation of semisolid extruded Al-4Cu-xMg alloy preforms have been performed in the present investigation. Pressureless sintering of Al-4 wt% Cu with varying Mg (0, 0.25, 0.5, 0.75 and 1 wt%) powder mixture was performed in an inert atmosphere. The sintering behavior and mechanical properties of all alloys were investigated at different temperatures and compositions, respectively. The maximum density and hardness (647 MPa) were found in the Al-4Cu-0.5 Mg composition sintered at 550 °C. Semisolid extrusion was carried out on sintered Al-4Cu-0.5 Mg alloys having an Initial Relative Density of 80% and 90% to find the structure-property correlation due to deformation. Extrusion was carried out in between solidus (542.7 °C) and liquidus (662.8 °C) temperatures with different solid fractions, which were derived from the TG/DTA analysis. All experiments were performed with low extrusion ratio of 1.44, strain rate of 0.1 s−1 and die approach angle of 30°. Structure-property correlation study of extruded rod was performed at front end, middle part and rear end to understand the temperature and metal flow profiles during deformation process. There were no visible defects found in the demarcated samples. The presence of Al2Cu precipitate in the extruded alloy was identified by X-Ray diffractometer and scanning electron microscope with energy dispersive spectroscopy analysis. The microstructural evolution was observed in all the extruded bars by optical microscope. Micro-hardness of extruded samples was found to be 1001 MPa, which was almost two times of sintered sample. [ABSTRACT FROM AUTHOR]

Reservoirs have been given priority as an important resource for fisheries enhancement in inland open waters. This paper described the spatial and temporal pattern of fish diversity using GIS platform, assemblage structure, and studied the influence of environmental parameters in these variables in a large tropical reservoir, Chandil, located in the eastern India using multiple approaches. Altogether, 42 fish species belonging to 30 genera were recorded from the reservoir, including two exotic species: pangas, Pangasianodon hypophthalmus, and Nile tilapia, Oreochromis niloticus. The diversity indices indicated lowest fish diversity in the lacustrine zone, but without distinct seasonal variation. The taxonomic distinctness was lower in the lacustrine zone as compared to other zones, and the summer season exhibited higher taxonomic distinctness, though the number of species was lowest. Most of the physicochemical parameters recorded are within the favorable range for fisheries enhancement. Among the environmental parameters, pH and nitrate significantly influenced the fish abundance. The study recommends suitable measures and interventions for conservation of the native fish diversity and sustainable fisheries development: closed season, protected/conservation area, introduction of fish aggregating device (FAD), and regulated fish culture in enclosures. [ABSTRACT FROM AUTHOR]

Tomato production in the Gangetic plains of eastern India is threatened by high incidence of bacterial wilt (BW) disease caused by Ralstonia solanacearum. Understanding the genetic control of tolerance through utilization of tolerant genotype is the prerequisite to frame effective resistant breeding strategy. Genetic control of host tolerance to BW disease was studied employing six generations (P1, P2, F1, F2, BC1, BC2) of two crosses between one tolerant (Utkal Kumari) and two susceptible (CLN-2498D and CLN-2777F) genotype(s) in presence of the predominant virulent strain (Biovar IIIA) of Ralstonia solanacearum through χ2 test and generation mean analysis. It emerged that tolerance to BW disease was conditioned predominantly by single dominant gene in two Tolerant × Susceptible crosses however, significance of scaling tests revealed involvement of duplicate epistasis suggesting somewhat complex inheritance pattern for the tolerance. Area under disease progress-curve (AUDPC) suggested that cumulative disease progress was less in tolerant genotypes of segregating generations. Duplicate epistasis controlling BW disease incidence suggested mild selection intensity in earlier generation, while intense in later generations of the segregating population in tomato. In this situation, modified bulk method of selection is recommended, in which selection is performed after attaining the homozygosity for maximum heterozygous loci. However, development of hybrids involving at least one tolerant parent is the best option for breeding tomato against BW disease. [ABSTRACT FROM AUTHOR]

The present work describes the microstructural changes and their impacts on the electrical resistivity, elastic modulus and damping property of a eutectic Sn-3.5wt% Ag solder material when exposed to high temperature. A detail microstructural study was conducted through the scanning electron microscopy with energy-dispersive spectroscopy analysis and electron backscattered diffraction technique. In as-cast eutectic Sn-Ag solder alloy, sub-micrometer size Ag3Sn intermetallic compound (IMC) particles and bamboo-like dendritic structure with a dimension of length 20-30 µm and width 3-5 µm formed during solidification. However, after thermal aging treatment at 150 °C for 60 days, the fine Ag3Sn IMC particles and β-Sn grain appeared with coarse microstructure with the formation of twinning having the <100> twin axis and 60° rotation. As a result, microstructure and Sn-crystal orientation of Sn-Ag solder greatly impact on its overall properties and turned inferior. From material properties evaluation, it was confirmed that the electrical resistivity, elastic and shear moduli values were significantly reduced with aging time. Consequently, the values of damping capacity improved due to the reduction of moduli. [ABSTRACT FROM AUTHOR]

The influence of geochemical processes and quality of groundwater in a rural tract of Damodar Valley region were investigated. The study has distinguished the groundwater as fresh, soft to moderately hard and mainly CaHCO3 type. The paired samples student’s t test shows the significant seasonal variations of pH, HCO3−, and Fe. Amphoteric exchange has lessened HCO3− concentration in post-monsoon which subsequently has caused to drop pH. Quite the reverse, the monsoon precipitation has triggered the additional release of Fe from iron-bearing sediments. The contaminant Cl− is from the domestic wastewater as is evidenced by field observations. The inter-variable relations, cation and anion mechanisms, and mineral saturation indices reveal that the dissolutions of silicate and carbonate minerals are the primary sources of major ions in groundwater. The chloro-alkaline indices showed the role of ion exchange too in water chemistry. The R-mode factor analysis also successfully identified two dominant processes regulating water chemistry—geogenic sources (Ca2+, Mg2+, Na+, and HCO3−) and anthropogenic inputs (mainly Cl−). The groundwater is found unsuitable for drinking at 82 and 93% of wells in pre- and post-monsoon seasons, respectively mainly due to elevated Fe content. The water from more than 90% of wells is appropriate for irrigation uses. The study recommends the proper treatment of contaminated water for consumption and measures to protect the groundwater from the waste water infiltration. [ABSTRACT FROM AUTHOR]

The performance of the product components in application greatly depends on the morphological parameters and inherent capabilities of the material. In the present study, Al-ZrB2 composite is made out of powder metallurgy route. Incremental weight% (0, 2, 4 and 6 wt%) of ZrB2 were added into Al matrix to produce different composites. Composites were prepared by cold axial compaction followed by pressureless sintering at 550 °C for 1 h in controlled atmosphere (Ar gas). Hardness increased with the amount of ZrB2 in the composite. To enhance the properties further, composites were deformed at 25, 400 and 500 °C respectively. The size, shape and orientation of the grains in the deformed composites were analyzed and correlated with the mechanical properties. The mechanical adhesion of ZrB2 particle with the Al matrix was examined in different composites during different temperature conditions of deformation process. The fracture strain of the composites decreased with increase of ZrB2 in the composite. [ABSTRACT FROM AUTHOR]