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Social media has become a platform for expressing opinions and emotions, but some people also use it to spread hate, targeting individuals, groups, communities, or countries. Therefore, there is a need to identify such content and take corrective action. During the last few years, several techniques have been developed to automatically detect and identify hate speech, offensive and abusive content from social media platforms. However, majority of the studies focused on hate speech detection in English language texts only. The non-availability of suitable datasets is a major reason for lack of research work in other languages. Hindi is one such widely spoken language where such datasets are not available. This work attempts to bridge this research gap by presenting a curated and annotated dataset for target-based hate speech (TABHATE) in the Hindi language. The suitability of the dataset is explored by applying some standard deep learning and transformer-based models for the task of hate speech detection. The experimental results obtained show that the dataset can be used for experimental work on hate speech detection of Hindi language texts. [ABSTRACT FROM AUTHOR]

In IoT applications, prediction models have fundamental challenges such as real-time processing, producing results with considerable/without delay, and taking action against pattern drift. While existing models can excel when data statistics remain relatively stable, real-time systems may encounter difficulties, particularly when confronted with dynamic shifts in data behavior. Analyzing data streams generated by different IoT applications and detecting complex pattern on the fly has become an open area of research. Complex event processing with adaptivity is a must to get desired features in such models. To address this issue, a comprehensive model for prediction has been proposed in this paper. It consists of two phases: (1) the basic model is constructed using historical data, (2) a fast Fourier transform-based adaptive support vector regression (FFT-ASVR) approach is proposed to predict events embedded in IoT data streams. FFT-ASVR predicts abnormal events by experiencing a change in data streams with real-time model updation. The performance of FFT-ASVR with a similar existing method SVM-RBF is presented using real-time traffic data of Madrid city. The proposed approach has significant improvement in terms of mean absolute percentage error (MAPE) for prediction, is adaptive in nature, and is also capable of handling the issue of pattern drift. [ABSTRACT FROM AUTHOR]

Altmetrics, or alternative metrics, refer to the newer kind of events around scholarly articles, such as the number of times the article is read, tweeted, mentioned in blog posts etc. These metrics have gained a lot of popularity during last few years and are now being collected and used in several ways, ranging from early measure of article impact to a potential indicator of societal relevance of research. However, there are several studies which have cautioned about use of altmetrics on account of quality and reliability of altmetric data, as they may be more prone to manipulations and artificial inflations. This study proposes a framework based on application of Benford's Law to evaluate the quality of altmetric data. A large sized altmetric data sample is considered and the fits with Benford's Law are computed. The analysis is performed by doing plots of the empirical data distributions and the theoretical Benford's, and by employing relevant statistical measures and tests. Results for fit on first and second leading digit of altmetric data show conformity to Benford's distribution. To further explore the usefulness of the framework, the altmetric data is subjected to artificial manipulations through a systematic process and the fits to Benford's law are reassessed to see if there are distortions. The results and analysis suggest that Benford's Law based framework can be used to test the quality of altmetric data. Relevant implications of the research are discussed. [ABSTRACT FROM AUTHOR]

Due to population growth and rapid urbanization in Indian cities, transportation has evolved as a critical concern affecting a large number of commuters everyday. Hence it is important for the urban planners, policymakers, and transportation authorities of India to know about the different public grievances/concerns regarding transportation. This study aims to uncover valuable information about specific transport-related complaints/grievances in Indian cities from the vast pool of user-generated content on social media platforms such as Twitter. As an initial step, we have explored the broad sentiment of commuters in six Indian metropolitan cities about the existing transportation systems, and created a dataset that broadly classify tweets into negative and positive sentiments. Next, we have identified a set of fine-grained complaints/grievances in these tweets, and thus created the first dataset containing transport-related tweets labelled into various specific complaints/grievances in a multi-label setting. To our knowledge, there is no existing dataset that labels tweets according to specific concerns raised in the posts. We apply several classification models on the dataset, for classifying transportation-related tweets into the specific complaints/grievances. We further conducted a city-wise analysis to better comprehend the specific transport-related complaints prevalent in each Indian city. [ABSTRACT FROM AUTHOR]

Automated classification of tumors in breast ultrasound (BUS) using deep learning-based methods has achieved remarkable success recently. The initial performance of the individual convolutional neural networks (CNNs) and vision-Transformer have been encouraging. However, artifacts like shadows, low contrast, speckle noise, and variations in tumor shape and sizes impose a kind of uncertainty that limits the performance of existing methods. The interpretation of classification results with a high confidence rate is strongly required in a real clinical setting. This work proposes an efficient method for predicting breast tumor malignancy (EPTM) in BUS images. EPTM comprises heterogeneous deep learning-based feature extraction models and a Choquet integral-based fusion mechanism. Specifically, EPTM incorporates efficient CNN and vision-Transformer methods to build accurate individual breast tumor malignancy prediction models (IMMs). The heterogeneous IMMs ensure complete feature representation of the breast tumors as each CNN or vision-Transformer captures different textural patterns in BUS images. EPTM applies learnable fuzzy measure-based Choquet integral to fuse the predictions of IMMs, where the gray wolf optimizer is employed for generating fuzzy measures. The experimental results confirm the versatility of the EPTM method evaluated on two publicly available datasets, namely UDIAT BUS and Baheya Hospital. EPTM yielded state-of-the-art results with the area under the curve of 0.932 and 0.98, respectively, in the UDIAT BUS and Baheya Hospital datasets. The source code of the proposed model is publicly available at https://github.com/vivek231/breastUS-classification. [ABSTRACT FROM AUTHOR]

The diversity of natural images in terms of visual features is useful in saliency detection. The complementary visual features jointly improve the performance of salient object detection. In this paper, we introduce a novel region-based feature combination approach that utilizes the diversity of visual features over image regions for robust salient object detection. The proposed approach works in four steps: (i) region formation, (ii) feature extraction, (iii) region-wise weight learning and (iv) region-based feature combination. Region formation is carried out using simple linear iterative clustering (SLIC) algorithm. Then, the features are extracted using Boundary Connectivity (BC), Contrast Cluster (CC), and Minimum Directional Contrast (MDC) methods. These features are then used for learning weights vectors for each region. Our major contribution is in step four where a novel dynamic weighted feature combination method is proposed. In this step region-wise integration weights are obtained by using a nature inspirited optimization algorithm called Constrained Particle swarm optimization (CPSO). Then salient features are region-wise combined with their dynamic relevance for final saliency map. The proposed method is compared with eight state-of-the-art saliency detection methods on five public available saliency benchmark datasets namely MSRA10K, DUT-OMRON, ECSSD, PASCAL, and SED2. The experimental results demonstrate that the proposed method performs better than state-of-the-art methods in terms of Precision, Recall, F-measure and Mean Absolute Error while comparable in terms of AUC and ROC curve. [ABSTRACT FROM AUTHOR]

The massive growth in scholarly outputs during the last few decades has resulted into the creation of several scholarly databases to index the outputs. These scholarly databases index publication records and provide different metadata fields for different kinds of usage ranging from retrieval and research evaluation to various scientometric analysis. The 'author keywords' is one such important metadata field provided by many databases and used for different text-based and thematic structure analysis. The Dimensions database, however, does not provide 'author keywords' metadata field, instead it provides automatically generated terms from the article full texts, called 'concepts'. Therefore, it is not clear whether different text-based analysis can be done with data provided by Dimensions database. Therefore, this article explores the distributional characteristics of Dimensions concepts. The Dimensions concept data obtained for a sufficiently large sample of scholarly articles is analysed through rank frequency distribution plots in the log–log space. Existence of Zipfian distribution is explored. The results indicate that Dimensions concepts adhere to the Zipfian properties which in turn indicates that Dimensions concepts have similar distributional characteristics as author keywords and hence they may have the same expressive power as that of author or index keywords for scientometric exercises. The study is novel as it is the first study to explore the distributional characteristics of the Dimensions concepts, particularly with respect to Zipfian properties, which provide the statistical foundation for understanding the Dimensions concepts and help to model and analyse them. [ABSTRACT FROM AUTHOR]

The advancements in computer vision and image processing techniques have led to emergence of new application in the domain of visual surveillance, targeted advertisement, content-based searching, human–computer interaction, etc. Out of the various techniques in computer vision, face analysis, in particular, has gained much attention. Several previous studies have tried to explore different applications of facial feature processing for a variety of tasks, including age and gender classification. However, despite several previous studies having explored the problem, the age and gender classification of in-wild human faces is still far from achieving the desired levels of accuracy required for real-world applications. This paper, therefore, attempts to bridge this gap by proposing a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. The proposed model's performance is compared with several state-of-the-art models proposed so far. An improvement of approximately 10% and 6% over the state-of-the-art implementations for age and gender classification, respectively, is noted for the proposed model. The proposed model is thus found to achieve superior performance and is found to provide a more generalized learning. The model can, therefore, be applied as a core classification component in various image processing and computer vision problems. [ABSTRACT FROM AUTHOR]

This paper presents the empirical correlations to predict the phase transition temperatures of CuAlMn based shape memory alloy thermal/heat switches. The correlations can precisely predict the start and finish phase transition temperatures of austenite and martensite lattice structures within the error range of ±18% at different Cu/Al and Cu/Mn compositions in CuAlMn shape memory alloy. Moreover, for the first time, contours on the effect of Cu/Al ratio on phase transition temperatures are analyzed in detail. It has been found that ‘Mn’ compassions in CuAlMn alloy play a critical role, and should be at the higher end of composition to achieve low-temperature actuation (<123 K), enhanced switching ratio, and overall performance improvement in shape memory alloy-based heat switches. The precise performance prediction of CuAlMn based shape memory alloy will be helpful in the design of effective mechanical heat switches for low-temperature space applications. [ABSTRACT FROM AUTHOR]

Magneto-caloric materials such as Beryllium, Gallium, Cadmium and Tungsten are most suitable for regulating the heat flow at a cryogenic temperature below 10 K. Among these, Tungsten is preferable as a switching element in magnetoresistive heat switches because of its high Debye temperature and low critical superconducting temperature. Machining tungsten is difficult because of its inherent properties. Due to this, WIRE Electrical Discharge Machining (WEDM) process is preferable to convert tungsten into the desired shape and size. Thus, current research has focused on the WEDM process. Before machining of material, parametric optimization is needed to reduce the operating cost, material wastage, number of experiments and time. Therefore, the present research has focused on optimizing the process parameters using the Taguchi Grey Analysis (TGA) method for the WEDM process of tungsten. In this method, seven input parameters such as pulse on, pulse off time, arc off time, water pressure, wire feed, wire tension, gap voltage, and three output parameters, such as material removal rate (MRR), Kerf width and surface roughness value, are chosen for parametric optimization. The parametric optimization study of machining single-crystal pure tungsten using WEDM signifies that the response parameters should (MRR is 0.298 mm3/min, Kerf width is 0.346 mm, Surface roughness is 1.834 μm). [ABSTRACT FROM AUTHOR]

Scientific collaboration at international level has increased manifolds during last two decades. Collaboration is not only associated with higher research productivity but has also been found to be positively correlated with impact. Considering the benefits and advantages of international collaboration for the national science and technology systems of a country, policymakers in different countries have designed programs to promote international collaboration in science. This paper attempts to measure and characterize the international collaboration patterns in Indian scientific research for the last 20 years (2001–2020) using data from WoS. The existing methods from scientometrics and a new formalism of boost in productivity, citations and visibility, is used for the purpose. Analytical results show that India's international collaboration has grown with a CAGR of 12.27% during this period, rising from 20.73% internationally collaborated papers in 2001 to 32.35% internationally collaborated papers in 2020. USA, Germany, England, and China remain the top collaborating partners in terms of volume of papers, however, the relative intensity of collaboration with South Korea and Saudi Arabia has increased significantly. Among the internationally collaborated papers of India, about 50% papers have an Indian researcher as lead (first) author, and further the internationally collaborated papers of India have more than 50% authors from India. The Indian indigenous scholarly ecosystem is found to be significantly productive, but slight and heavy dependence for impact and visibility, respectively, is found on international collaborations. The probable factors shaping the Indian international collaboration and the major policy implications for India are discussed. [ABSTRACT FROM AUTHOR]

The synthesis of secondary metabolites is a constantly functioning metabolic pathway in all living systems. Secondary metabolites can be broken down into numerous classes, including alkaloids, coumarins, flavonoids, lignans, saponins, terpenes, quinones, xanthones, and others. However, animals lack the routes of synthesis of these compounds, while plants, fungi, and bacteria all synthesize them. The primary function of bioactive metabolites (BM) synthesized from endophytic fungi (EF) is to make the host plants resistant to pathogens. EF is a group of fungal communities that colonize host tissues' intracellular or intercellular spaces. EF serves as a storehouse of the above-mentioned bioactive metabolites, providing beneficial effects to their hosts. BM of EF could be promising candidates for anti-cancer, anti-malarial, anti-tuberculosis, antiviral, anti-inflammatory, etc. because EF is regarded as an unexploited and untapped source of novel BM for effective drug candidates. Due to the emergence of drug resistance, there is an urgent need to search for new bioactive compounds that combat resistance. This article summarizes the production of BM from EF, high throughput methods for analysis, and their pharmaceutical application. The emphasis is on the diversity of metabolic products from EF, yield, method of purification/characterization, and various functions/activities of EF. Discussed information led to the development of new drugs and food additives that were more effective in the treatment of disease. This review shed light on the pharmacological potential of the fungal bioactive metabolites and emphasizes to exploit them in the future for therapeutic purposes. [ABSTRACT FROM AUTHOR]

Significant efforts have been made to monitor human activity, although it remains a challenging area for computer vision research. This paper has introduced a framework to identify the most common types of video surveillance activities. The proposed framework consists of three consecutive modules: (i) human detection by background subtraction, (ii) retrieval of uniform and rotation invariant local binary pattern (LBP) feature, and (iii) identification of human activities with a support vector machine (SVM) multiclass classifier. This framework provides a consistent view of the human actions that look at multiple subjects from different views. In addition to this, uniform patterns provide better performance in discriminating human activities. A multiclass SVM is used for classification of human activities. SVM classifier is set and trained to achieve the better efficiency by selecting the appropriate feature before it is integrated. Weizmann's Multiview dataset, CASIA dataset and IXMAS dataset confirm the high efficiency and better robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

Scientific collaboration at regional and international levels has increased manifolds during the last 2 decades. The South Asian region, comprising of Afghanistan, Bangladesh, Bhutan, India, Maldives, Pakistan and Sri Lanka, habitats a significant part of the world population, and is emerging as a major knowledge producer. These South Asian countries are not only connected through shared history, language and culture, but also through an intergovernmental organization called South Asian Association for Regional Cooperation (SAARC). This article attempts to measure and characterize the research collaboration in the SAARC countries during 2001–2019. The research publication data for analysis is obtained from the Web of Science database. Different kinds of collaboration- inter, mixed, intra and domestic- among the SAARC countries are measured and analyzed through a computational analysis. Results indicate that SAARC countries collaborate more with countries outside the region than within the region. The within region collaboration has grown in volume but is still less than 1% of the total research output from the region. The collaboration is also found to vary across subject areas, with Social Science & Mathematics having higher proportion of international collaboration, Medical Science & Physics having higher mixed-collaboration, and Social Science & Environment Science having higher intra collaboration. Major implications of the results are discussed. [ABSTRACT FROM AUTHOR]

To gain insights into the idiosyncrasies of CD34 + enriched leukemic stem cells, we investigated the nature and extent of transcriptional heterogeneity by single-cell sequencing in pediatric AML. Whole transcriptome analysis of 28,029 AML single cells was performed using the nanowell cartridge–based barcoding technology. Integrated transcriptional analysis identified unique leukemic stem cell clusters of each patient and intra-patient heterogeneity was revealed by multiple LSC-enriched clusters differing in their cell cycle processes and BCL2 expression. All LSC-enriched clusters exhibited gene expression profile of dormancy and self-renewal. Upregulation of genes involved in non-coding RNA processing and ribonucleoprotein assembly were observed in LSC-enriched clusters relative to HSC. The genes involved in regulation of apoptotic processes, response to cytokine stimulus, and negative regulation of transcription were upregulated in LSC-enriched clusters as compared to the blasts. Validation of top altered genes in LSC-enriched clusters confirmed upregulation of TCF7L2, JUP, ARHGAP25, LPAR6, and PRDX1 genes, and serine/threonine kinases (STK24, STK26). Upregulation of LPAR6 showed trend towards MRD positive status (Odds ratio = 0.126; 95% CI = 0.0144–1.10; p = 0.067) and increased expression of STK26 significantly correlated with higher RFS (HR = 0.231; 95% CI = 0.0506–1.052; p = 0.04). Our findings addressed the inter- and intra-patient diversity within AML LSC and potential signaling and chemoresistance-associated targets that warrant investigation in larger cohort that may guide precision medicine in the near future. [ABSTRACT FROM AUTHOR]

Tyrosine kinase inhibitors (TKIs) are highly effective in treating chronic myelogenous leukemia (CML). However, primary and acquired drug resistance to TKIs have been reported. In this study, we used RNA sequencing followed by RQ-PCR to show that the proto-oncogene EVI1 targets the drug-metabolizing gene PTGS1 in CML. The PTGS1 promoter element had an EVI1 binding site, and CHIP assay confirmed its presence. Data from a publicly available CML microarray dataset and an independent set of CML samples showed a significant positive correlation between EVI1 and PTGS1 expression in CML. Downregulation of EVI1 in K562 cells and subsequent treatment with TKIs resulted in a lower IC50 in the control cells. Furthermore, combined inhibition of BCR-ABL with imatinib and PTGS1 with FR122047 (PTGS1 inhibitor) synergistically reduced the viability of imatinib-resistant K562 cells. We conclude that elevated EVI1 expression contributes to TKIs resistance and that combined inhibition of PTGS1 and BCR-ABL may represent a novel therapeutic approach. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed complex Fourier–Bessel series expansion-based empirical wavelet transform (CFBSE-EWT) and Hilbert spectral analysis (HSA) for time-frequency analysis of complex-valued signals. The proposed method obtains the real-valued positive and negative frequency components of the complex-valued signal using a suitable filter. Further, the obtained real-valued components are decomposed into corresponding set of subband signals using the Fourier–Bessel series expansion-based empirical wavelet transform (FBSE-EWT) method. The HSA is applied on the subband signals to obtain the time-frequency distribution (TFD). The effectiveness of the proposed CFBSE-EWT has been evaluated on two synthetic multicomponent complex-valued signals and a real-life wind signal. The decomposition results of CFBSE-EWT method are also compared with complex empirical mode decomposition (CEMD), complex flexible analytic wavelet transform (CFAWT), complex variational mode decomposition (CVMD), and complex improved eigenvalue decomposition of Hankel matrix (CIEVDHM) using the quality of reconstruction factor as performance objective measure. Additionally, the TFD of the synthetic complex-valued signals and real-life complex-valued wind signal is obtained from the proposed CFBSE-EWT-based HSA and compared with the CEMD-based HSA, CFAWT-based HSA, CVMD-based HSA, and CIEVDHM-based HSA methods. The CFBSE-EWT-based HSA provides improved TFD and it is useful for analysis of real-life complex-valued signals. [ABSTRACT FROM AUTHOR]

The present study evaluates different physical parameterization schemes available in Weather Research and Forecasting model version 3.8 (WRF-ARW) over the Angul-Talcher region of Odisha state in India. Sensitivity analysis has been carried out for various schemes for planetary boundary layer, microphysics, and radiation in the WRF model, which have not been tested over this Indian region that often experiences extreme heat in summer (> 45 °C) as well as high air pollution. Performance evaluation is based on in situ observations of near-surface temperature, wind speed, and relative humidity during a representative case of peak summer conditions. It was inferred from analysis of several sets of simulations that the combination of GBM PBL scheme, Lin microphysics, and RRTMG radiation scheme performs better for near surface parameters such as temperature at 2 m (overall RMSE of 2.19 °C, in daytime and 2.06 °C in nighttime), relative humidity (RMSE: 20.65%), and wind in comparison to the rest of the combinations. Furthermore, the various schemes were also tested with observed vertical profile of wind speed, temperature, and heat fluxes, to arrive at the overall conclusions of this study. [ABSTRACT FROM AUTHOR]

Collaboration in scientific research is believed to produce more useful and impactful research. The collaboration may involve multiple researchers from one institution or researchers from different institutions. Many times, such collaborations involve institutions belonging to different categories (say University, Industry or Government). This paper attempts to analyse the University–Industry–Government collaboration in research to find out whether such collaborated research outputs attract higher bibliometric and altmetric impact. Research output data of Indian institutions for the period 2010–2018 obtained from Web of Science database is used to demonstrate the analysis. The institutions are programmatically and manually tagged into one of the three categories (University, Industry or Government) depending on their type, and the research output involving different kinds of collaboration are identified and analysed. The results indicate that research papers involving University–Industry–Government collaboration do not differ significantly in terms of citations as compared to non-collaborated papers. However, an advantage in terms of social media mentions is found for different types of University-Industry-Government collaborated papers. Collaboration between U and I category entities, G and I category entities and the UIG collaboration is seen to get advantage in terms of mentions as compared to papers that do not involve such collaboration. Probable reasons for the observed patterns and implications of the results are discussed towards the end of the paper. [ABSTRACT FROM AUTHOR]

Salient object detection is a challenging research area, which aims to highlight significant region of the visual scene more accurately and quickly. In this research direction, we propose a novel saliency detection model called Convex Hull Based Random Walks (CoBRa) approach. In the proposed model, an image is segmented into superpixels and Convex Hull is constructed based on the segmented image to roughly partition the segmented image into two regions: CH-foreground and CH-background regions and the centroid of the CH-foreground region is calculated. Then, initial saliency is computed by using two priors viz. contrast and center priors. Here, the proposed model exploits CH-foreground region centroid obtained by Convex Hull to computed center prior which is more efficient than image center. Afterwards, two thresholds are empirically obtained and applied on initial saliency map to produce two binary segmented images. Based on these two binary images, the proposed model collects foreground and background seeds. These seeds are further refined with CH-foreground and CH-background regions to produce reliable and effective seeds. Finally, a random walk is constructed with the determined seeds to generate a pixel-wise saliency map. The superiority of the proposed model is validated via extensive experimental results performed on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K. The performance of the proposed model was compared with eight state-of-the-art methods in terms of Precision, Recall, F-Measure, Receiver Operating Characteristics (ROC), and Area under the curve (AUC). The proposed method outperforms or comparable with compared methods in terms of all the performance measures. [ABSTRACT FROM AUTHOR]

Spheroidization is a common technique to improve the formability and machinability of material. Multiple combinations of deformation level and annealing time were employed to optimize the microstructure in 22MnB5 steel, aiming to achieve maximum spheroidization and keeping the process most economical. The critical annealing temperature for spheroidization was decided through differential scanning calorimetry measurement. Spheroidization was achieved by annealing of as-received and three different amounts of cold-rolled material, namely 10, 20, and 30% reduction in thickness. Annealing was done at 720 °C for four different times, 10, 20, 40, and 60 h, at all deformation levels. Convoluted multiple whole profile fitting technique of x-ray diffraction pattern was used to estimate the stored dislocation density in the material after different deformation levels. The microstructural investigation was performed using optical microscopy, scanning electron microscopy and EBSD techniques. The extent of spheroidization was measured through the average aspect ratio of cementite precipitate. Mechanical properties were measured through surface and core micro-hardness of initial and spheroidized samples. Microstructural characterization showed that deformation produced sub-grains and low angle grain boundaries, facilitating an easy path for diffusion of carbon. However, it was also found that a very high amount of pre-strain is also detrimental to the spheroidization process because it results in the formation of very long and sharp cementite lamella, which required more annealing time for spheroidization. The best processing parameter for spheroidization of 22MnB5 steel, considering the optimum spheroidization and minimum cost of the process, was found as 20% cold rolling reduction followed by 20 h annealing. [ABSTRACT FROM AUTHOR]

Autophagy is a lysosomal degradation pathway that is constitutively active in almost every cell of our body at basal level. This self-eating process primarily serves to remove superfluous constituents of the cells and recycle the degraded products. Autophagy plays an essential role in cell homeostasis and can be enhanced in response to stressful conditions. Impairment in the regulation of the autophagic pathway is implicated in pathological conditions such as neurodegeneration, cardiac disorders, and cancer. However, the role of autophagy in cancer initiation and development is controversial and context-dependent. Evidence from various studies has shown that autophagy serves dual purpose and may assist in cancer progression or suppression. In the early stages of cancer initiation, autophagy acts as a quality control mechanism and prevents cancer development. When cancer is established and progresses to a later stage, autophagy helps in the survival of these cells through adaptation to stresses, including exposure to anti-cancer drugs. In this review, we highlight various studies on autophagic pathways and describe the role of autophagy in cancer, specifically acute myeloid leukemia (AML). We also discuss the prognostic significance of autophagy genes involved in AML leukemogenesis and implications in conferring resistance to chemotherapy. [ABSTRACT FROM AUTHOR]

The article presents an introduction to a newly created scientometric portal called Indian Science Reports, available at www.indianscience.net. The portal is designed to fulfil the need for a single integrated resource for analytical data about research competencies of India at an overall level as well as Indian institutions at an individual level. India's research performance in terms of research output, citations, highly cited papers, international collaboration, open access levels, gender distribution and social media visibility etc. are computationally analysed using publication metadata collected from Dimensions database. The portal also provides a mechanism to look for research performance of all major Indian higher education institutions, on various standard parameters, through an institutional search. Further, a concept-based search is integrated to identify top performing Indian institutions on a given research topic. The portal, thus, provides an invaluable resource of Indian scientific research data and information, which can be used for various purposes ranging from scientometric evaluation to thrust area-based funding decisions. [ABSTRACT FROM AUTHOR]

Scientific journals are currently the primary medium used by researchers to report their research findings. The transformation of print journals into e-journals during the last two decades has not only simplified the process of submissions to journals but has also increased their access across the world. It is well-known that there are significant differences in the total number of journals indexed from different countries. It is, however, not very concretely known whether the lack of appropriate number of publication venues in a country (including in one or more subject areas) may inhibit its publication propensity in one way or other. This article, therefore, attempts to explore the relationship between the number of journals indexed from a country and its research output. Scopus database is used as reference database and the master journal list of Scopus is analysed to identify number of journals indexed from 50 selected countries, that have significant volume of research output. The publication data for the countries is obtained from Scopus. The following major relationships are observed: (a) number of journals from a country and its research output, (b) growth rate of journals and research output for different countries, (c) global share of journals and research output for different countries, and (d) subject area-wise number of journals and research output in that subject area for different countries. The results show that for majority of the countries, the number of journals indexed is positively correlated to their research output volume. A similar relationship is also observed in the subject area-wise analysis, confirming existence of the positive correlations between number of journals in a subject area and the research output in that subject area. However, several countries do not fully conform to the observed relationship, indicating that there are several other factors driving the research output of a country. The study, at the end, presents a discussion of the outcomes and provides implications for policy perspectives for different countries. [ABSTRACT FROM AUTHOR]

Transgenic crops expressing Cry δ-endotoxins of Bacillus thuringiensis for insect resistance have been commercialized worldwide with increased crop productivity and spectacular socioeconomic gains. To attain the enhanced level of protein expression, the cry genes have to be extensively modified for RNA stability and translation efficiency in the plant systems. However, such modifications in nucleotide sequences make it difficult to express the cry genes in Escherichia coli because of the presence of E. coli rare codons. Induction of gene expression through the T7 promoter/lac operator system results in high levels of transcription but limits the availability of activated tRNA corresponding to rare codons that leads to translation stalling at ribosomes. In the present study, an Isopropyl ß-D-1-thiogalactopyranoside (IPTG)/rifampicin combination-based approach was adopted to induce transcription of cry genes through T7 promoter/lac operator while simultaneously inhibiting the transcription of host genes through rifampicin. The results show that the IPTG/rifampicin combination leads to high-level expression of four plant codon-optimized cry genes (cry2Aa, cry1F, cry1Ac, and cry1AcF). Northern blot analysis of the cry gene expressing E. coli samples showed that the RNA expression level in the IPTG-induced samples was higher as compared to that in the IPTG/rifampicin-induced samples. Diet overlay insect bioassay of IPTG/rifampicin-induced Cry toxins with Helicoverpa armigera larvae showed bioactivity (measured as LC50) similar to the previous studies. The experiment has proved that recombinant synthetic gene (plant codon-optimized gene) with the combination of Rifampicin which inhibits DNA-dependent bacterial RNA polymerase and reduces the excessive baggage of translational machinery of the bacterial cell triggers the production of synthetic protein. Purification of protein using high pH buffer increases the solubility of the protein. Further, LC50 analysis shows no reduction of protein activity leads to protein stability. Further, purified cry toxin protein can be used for crop protection against pests and a purified form of the synthetic protein can be used for antibody production and perform the immunoassay for the identification of the transgenic plant. The crystallographic structure of synthetic protein could be used for interaction study with another insect to see insecticidal activity. [ABSTRACT FROM AUTHOR]

ResearchGate has emerged as a popular professional network for scientists and researchers in a very short span. Similar to Google Scholar, the ResearchGate indexing uses an automatic crawling algorithm that extracts bibliographic data, citations, and other information about scholarly articles from various sources. However, it has been observed that the two platforms often show different publication and citation data for the same institutions, journals, and authors. While several previous studies analysed different aspects of ResearchGate and Google Scholar, the quantum of differences in publications, citations, and metrics between the two and the probable reasons for the same are not explored much. This article, therefore, attempts to bridge this research gap by analysing and measuring the differences in publications, citations, and different metrics of the two platforms for a large data set of highly cited authors. The results indicate that there are significantly high differences in publications and citations for the same authors captured by the two platforms, with Google Scholar having higher counts for a vast majority of the cases. The different metrics computed by the two platforms also differ in their values, showing different degrees of correlation. The coverage policy, indexing errors, author attribution mechanism, and strategy to deal with predatory publishing are found to be the main probable reasons for the differences in the two platforms. [ABSTRACT FROM AUTHOR]

The currently prevailing international ranking systems for institutions are limited in their assessment as they only provide assessments either at an overall level or at very broad subject levels such as Science, Engineering, Medicine, etc. While these rankings have their own usage, they cannot be used to identify best institutions in a specific subject (say Computer Science) by taking into account their performance in different thematic areas of research of the given subject (say Artificial Intelligence or Machine Learning or Computer Vision etc. for the subject Computer Science). This paper tries to bridge this gap by proposing a framework that uses the NLP and Network approach for identifying the core competency of institutions and their thematic research strengths. The core competency can be viewed as a measure of breadth of research capability of an institution in a given subject, whereas thematic research strength can be viewed as depth of research of the institution in a specific theme of a subject. The working of the framework is demonstrated in the area of Computer Science for 195 Indian institutions. The framework can be useful for institutions and the scientometrics research community as a system providing a detailed assessment of the core competency and the research strengths of institutions in different thematic areas. The framework and outcomes can also be useful for funding agencies in devising programs for 'performance-based funding' in 'thrust areas' or 'national priority areas'. [ABSTRACT FROM AUTHOR]

In this paper, a new method for the cross-term-free Wigner–Ville distribution (WVD) is proposed. The proposed method is based on sliding eigenvalue decomposition (SEVD) and the WVD which has been termed as SEVD–WVD. The SEVD decomposes the multicomponent signal into a set of monocomponent signals, and then, the WVD of analytic monocomponents is added to obtain a cross-term-free time–frequency representation. We have applied the proposed technique on clean and noisy synthetic signals in order to verify its robustness. Moreover, the SEVD–WVD is applied on speech signal to show its suitability on real-world data. The proposed method has been compared with other cross-term reduction techniques for the WVD in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for land-use land cover (LULC) simulation for urban growth estimation. The framework couples Cellular Automata Markov (CAM) and agent-based modeling (ABM) to explore the impact of socioeconomic factors, spatial neighborhoods, stakeholder choices, and development plans on LULC. The approach applies CA model to examine the spatiotemporal change in LULC patterns and ABM to observe the role of different socioeconomic drivers and commercial factors in the simulated environment to predict future urban LULC. For Dehradun city of India, the analysis of spatial patterns shows spatial accuracy of 87.86%. Most of the urban agriculture and vacant areas are converted to commercial, low-, medium-, and high-density residential areas. The coupled CAM-ABM model was found more efficient in prediction compared to the traditional CAM model. The methodologies presented in the paper can help decision makers foresight the requirements, thus leading to better resource management and informed decision making. [ABSTRACT FROM AUTHOR]

Salient object detection is a challenging task in the computer vision due to complexity of images and insufficiency of a single feature for saliency map generation. The performance of saliency detection depends upon the manner in which these visual features are combined. In this paper, we propose an efficient features combination model in which different features are combined in an appropriate manner. To meet this objective, a metaheuristic optimization algorithm called teacher-learning-based optimization (TLBO) is employed to find an optimal weight vector for feature combination. TLBO is a parameterless, efficient, and robust algorithm as it does not require parameter tuning during its implementation. Furthermore, the optimization criteria of TLBO is modified using a novel fitness function for improved performance. To check the performance of the proposed model, we have conducted extensive experiments over six benchmark datasets and compared the results against seven state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The entanglement entropy of many quantum systems is difficult to compute in general. They are obtained as a limiting case of the Rényi entropy of index m, which captures the higher moments of the reduced density matrix. In this work, we study pure bipartite states associated with S3 complements of a two-component link which is a connected sum of a knot K and the Hopf link. For this class of links, the Chern-Simons theory provides the necessary setting to visualise the m-moment of the reduced density matrix as a three-manifold invariant Z( M K m ), which is the partition function of M K m . Here M K m is a closed 3-manifold associated with the knot K m, where K m is a connected sum of m-copies of K (i.e., K # K ... # K ) which mimics the well-known replica method. We analayse the partition functions Z( M K m ) for SU(2) and SO(3) gauge groups, in the limit of the large Chern-Simons coupling k. For SU(2) group, we show that Z( M K m ) can grow at most polynomially in k. On the contrary, we conjecture that Z( M K m ) for SO(3) group shows an exponential growth in k, where the leading term of ln Z( M K m ) is the hyperbolic volume of the knot complement S3\ K m. We further propose that the Rényi entropies associated with SO(3) group converge to a finite value in the large k limit. We present some examples to validate our conjecture and proposal. [ABSTRACT FROM AUTHOR]

This paper deals with a class of polyol Solid–Solid Phase Change Materials (SS-PCMs) where neopentyl glycol is mixed in 0.5, 1, and 2 wt.% of multi-walled carbon nanotube (CNT) for thermal management of avionics cooling applications in spatial navigation systems. The SS-PCM was mixed with CNT by a physical blending method to obtain a homogeneous mixture. The surface morphology, chemical composition, crystal phase identification, thermal degradation, and phase change phenomena were studied through SEM, FTIR, XRD, TGA, and DSC, respectively. The phase transition temperatures and enthalpies of neopentyl glycol are found to be 50.80 °C and 104.04 J/g, which is ideally suitable for the optimum performance of avionic components. The thermal diffusivity of the samples was determined by using a LFA, and by interpolating the specific heat values and density, the thermal conductivity value was calculated. The experimental investigation was carried out through heat sink upon the addition of CNT in 0.5, 1, and 2 wt.% of NPG for 10 and 15 W heat dissipation rate under 3600 s ON/OFF duty cycle for different case studies. The effect of heat transfer enhancement was experimentally analyzed through longitudinal finned conventional heat sink for different samples to suit best for thermal control in spatial navigation systems. Further, it was found that a better reduction in temperature was achieved for SS-PCM/2wt.% CNT for all the cases. In conclusion, the composite mixture SS-PCM/CNT is suitable as a passive thermal control option for avionics cooling applications in the navigation of satellites. [ABSTRACT FROM AUTHOR]

Weaving knots W(p, n) of type (p, n) denote an infinite family of hyperbolic knots which have not been addressed by the knot theorists as yet. Unlike the well known (p, n) torus knots, we do not have a closed-form expression for HOMFLY-PT and the colored HOMFLY-PT for W(p, n). In this paper, we confine to a hybrid generalization of W(3, n) which we denote as W ̂ 3 (m, n) and obtain closed form expression for HOMFLY-PT using the Reshitikhin and Turaev method involving ℛ -matrices. Further, we also compute [r]-colored HOMFLY-PT for W(3, n). Surprisingly, we observe that trace of the product of two dimensional ℛ ̂ -matrices can be written in terms of infinite family of Laurent polynomials V n , t q whose absolute coefficients has interesting relation to the Fibonacci numbers ℱ n . We also computed reformulated invariants and the BPS integers in the context of topological strings. From our analysis, we propose that certain refined BPS integers for weaving knot W(3, n) can be explicitly derived from the coefficients of Chebyshev polynomials of first kind. [ABSTRACT FROM AUTHOR]

Traditionally, Web of Science and Scopus have been the two most widely used databases for bibliometric analyses. However, during the last few years some new scholarly databases, such as Dimensions, have come up. Several previous studies have compared different databases, either through a direct comparison of article coverage or by comparing the citations across the databases. This article aims to present a comparative analysis of the journal coverage of the three databases (Web of Science, Scopus and Dimensions), with the objective to describe, understand and visualize the differences in them. The most recent master journal lists of the three databases is used for analysis. The results indicate that the databases have significantly different journal coverage, with the Web of Science being most selective and Dimensions being the most exhaustive. About 99.11% and 96.61% of the journals indexed in Web of Science are also indexed in Scopus and Dimensions, respectively. Scopus has 96.42% of its indexed journals also covered by Dimensions. Dimensions database has the most exhaustive journal coverage, with 82.22% more journals than Web of Science and 48.17% more journals than Scopus. This article also analysed the research outputs for 20 selected countries for the 2010–2018 period, as indexed in the three databases, and identified database-induced variations in research output volume, rank, global share and subject area composition for different countries. It is found that there are clearly visible variations in the research output from different countries in the three databases, along with differential coverage of different subject areas by the three databases. The analytical study provides an informative and practically useful picture of the journal coverage of Web of Science, Scopus and Dimensions databases. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Aggregating efficient transformer and CNN networks using learnable fuzzy measure for breast tumor malignancy prediction in ultrasound images" published in the journal Neural Computing & Applications. The correction addresses an error in the affiliation details of one of the authors, Ehab Mahmoud Mohamed. The correct affiliation should be "Department of Electrical Engineering, College of Engineering, Wadi Alddwasir, Prince Sattam Bin Abdulaziz University, Wadi Alddwasir 11991, Saudi Arabia" instead of "Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt." The original article has been corrected. The publisher, Springer Nature, maintains a neutral stance on jurisdictional claims and institutional affiliations. The authors of the article are Vivek Kumar Singh, Ehab Mahmoud Mohamed, and Mohamed Abdel-Nasser. [Extracted from the article]

The increased social media attention to scholarly articles has resulted in creation of platforms & services to track the social media transactions around them. Altmetric.com and PlumX are two such popular altmetric aggregators. Scholarly articles get mentions in different social platforms (such as Twitter, Blog, Facebook) and academic social networks (such as Mendeley, Academia and ResearchGate). The aggregators track activity and events in social media and academic social networks and provide the coverage and transaction data to researchers for various purposes. Some previous studies have compared different altmetric aggregators and found differences in the coverage and mentions captured by them. This paper attempts to revisit the question by doing a large-scale analysis of altmetric mentions captured by the two aggregators, for a set 1,785,149 publication records from Web of Science. Results obtained show that PlumX tracks more altmetric sources and captures altmetric events for a larger number of articles as compared to Altmetric.com. However, the coverage and average mentions of the two aggregators, for the same set of articles, vary across different platforms, with Altmetric.com recording higher mentions in Twitter and Blog, and PlumX recording higher mentions in Facebook and Mendeley. The article also analysed coverage and average mentions captured by the two aggregators across different document types, subjects and publishers. [ABSTRACT FROM AUTHOR]

Machine learning based saliency detection methods have achieved better performance than traditional methods. Here, we propose a machine learning based method that utilizes Convex Hull and Extreme Learning Machine (ELM) for detecting salient object(s) in an image. The novelty of the proposed method lies in the generation of training set without using human annotations. Initially, an input image is segmented using SLIC algorithm at different scales to produce multiscale segmented images. This is followed by estimating two different saliency priors viz. (a) Convex Hull center prior and (b) contrast prior for each segmented image. These priors exploit foreground center and spatially weighted contrast respectively. Both of these estimated priors help in computing initial saliency of each segment across all scales. For each scale, the initial saliency map along with the Convex Hull based label map is employed on a segmented image to determine the positive (salient) and negative (background) training set. Distinctive features for each segment belonging to the training set are extracted and then passes to the Extreme Learning Machine for learning the ELM model. Afterwards, multiscale saliency maps of an image are found by applying the learned ELM model on distinctive features extracted from each segment across multiple scales. These multiscale saliency maps are linearly combined to obtain the final saliency map. The effectiveness of the proposed method is supported through extensive experimental results performed on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K. The performance of the proposed method was compared with 11 state-of-the-art methods in terms of Precision, Recall, F-Measure, Receiver Operating Characteristics (ROC), and Area under the curve (AUC). The proposed method outperforms or comparable with compared methods in terms of all the performance measures. [ABSTRACT FROM AUTHOR]

Salient object detection in a real-time environment demands high accuracy with less computation time. It is a changeling task to investigate a saliency model which improves saliency detection accuracy as well as reduces computation time. In this paper, we propose a hybrid model that improves detection accuracy with low computational time. The input image is simplified and clustered at multiple scales using SLIC and K-means clustering algorithms respectively. Gaussian Mixture Model (GMM) is developed on various color components of the digital image at multiple scales. The parameters of GMM are learnt using Expectation Maximization (EM) algorithm. The spatial variance of each color component is determined using GMM parameters and hence object position is estimated. Further, spatial variance of color components and object position is exploited to compute saliency map at a scale level. Afterwards, all the saliency maps generated across various scales are linearly combined to produce the final saliency map. The performance of the proposed model is compared in terms of Precision, Recall, F-Measure, Area under the Curve (AUC), Receiver Operating Characteristics (ROC) and Mean Absolute Error (MAE). Extensive experiments on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K show that the proposed model outperforms or comparable against 11 state-of-the-art methods of the last decade. The key features of the proposed method are object completeness and efficiency in terms of computational time. [ABSTRACT FROM AUTHOR]

Using event-by-event fluctuations, we study the diffusion parameters of net-charge, net-pion, net-kaon, and net-proton in the heavy-ion jet interaction generator (HIJING), and ultra-relativistic quantum molecular dynamics (UrQMD) models at different collision energies s NN available at BNL Relativistic Heavy Ion Collider (RHIC). The diffusion parameter (σ ) of net-charge and identified particles are estimated in rapidity space at various s NN . It is observed that, the σ values are independent of collision energies but emphasises the particle-species dependence of diffusion coefficient in the QGP medium. The present work on particle-species dependence of diffusion coefficient provides a baseline for comparison with the experimental data. [ABSTRACT FROM AUTHOR]

Social media platforms have now emerged as an important medium for wider dissemination of research articles; with authors, readers and publishers creating different kinds of social media activity about the article. Some research studies have even shown that articles that get more social media attention may get higher visibility and citations. These factors are now persuading journal publishers to integrate social media plugins in their webpages to facilitate sharing and dissemination of articles in social media platforms. Many past studies have analyzed several factors (like journal impact factor, open access, collaboration etc.) that may impact social media attention of scholarly articles. However, there are no studies to analyze whether the presence of social media plugin in a journal could result in higher social media attention of articles published in the journal. This paper aims to bridge this gap in knowledge by analyzing a sufficiently large-sized sample of 99,749 articles from 100 different journals. Results obtained show that journals that have social media plugins integrated in their webpages get significantly higher social media mentions and shares for their articles as compared to journals that do not provide such plugins. Authors and readers visiting journal webpages appear to be a major contributor to social media activity around articles published in such journals. The results suggest that publishing houses should actively provide social media plugin integration in their journal webpages to increase social media visibility (altmetric impact) of their articles. [ABSTRACT FROM AUTHOR]

Most of the urban heat island (UHI) studies are carried out in densely populated cities but core industrial areas are also potential sites of heat island effect despite having a comparatively lower population. In the present study, heat island assessment has been carried out for Angul-Talcher industrial area (ATIA) which is one of the oldest industrial areas of India and is still undergoing a transformation to accommodate more industries and mining operations. As the major contributors towards influencing local meteorology were expected to be industrial (and mining) activities, the heat island was studied as "industrial heat island" (IHI) rather than urban heat island. Industrial and mining sites were the most frequent nighttime canopy-layer heat island intensity (HIN) hotspots due to anthropogenic heat of associated industrial processes as well as built structures. During the daytime, croplands experienced the most frequent canopy-layer HIN hotspots which could be attributed to low moisture of the soils during the non-farming period of the field campaign. Hourly maximum atmospheric heat island intensities were observed in the range of 7–9 °C. Monthly maximum HINs ranged from 2.97 to 4.04 °C while 3-month mean HINs varied from 1.45 to 2.74 °C. Amongst different land use/land cover classes, the highest mean canopy-layer heat island intensity for the entire 3-month-long duration of field campaign during nighttime was assessed at the mining sites (3-month mean 2.74 °C) followed in decreasing order by the industrial sites (2.52 °C), rural and urban settlements (2.13 °C), and croplands (2.06 °C). Corresponding daytime canopy-layer heat island intensity was highest for the croplands (2.07 °C) followed in decreasing order by the mining sites (1.70 °C), rural and urban settlements (1.68 °C), and industry (1.45 °C). [ABSTRACT FROM AUTHOR]

Salient object detection is a challenging task, and several methods have been proposed for the same in the literature. The problem lies in that most of the methods perform good on a particular set of images but fail when exposed to a variety of different set of images. Here, we address this problem by proposing a novel framework called saliency bagging for detecting salient object(s) in digital images across a variety of images in a robust manner. The proposed framework generates the saliency map of an image in three phases: (i) Selection of existing saliency detection models and generation of initial saliency maps (ii) Generation of integrated binary map from the initial saliency maps by applying adaptive thresholding and majority voting (iii) Computation of final saliency map using integrated binary map and initial saliency maps by applying proposed integration logic. Extensive experiments on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K have been performed to determine the effectiveness of the proposed method. The performance of the proposed method is measured in terms of Precision, Recall, F-Measure, Mean Absolute Error (MAE) and Receiver Operating Characteristic (ROC) curve and compared with 25 state-of-the-art methods including 17 classic best-performing methods of the last decade, five existing selected, and three aggregation saliency methods. The proposed method outperforms all the compared classic and existing selected methods in terms of Precision, F-Measure, and MAE, while it is comparable to the best-performing methods in terms of Recall and ROC curve across all the six datasets. The proposed framework is computationally very fast than all compared aggregation methods, while performance is almost same on all datasets that support its superiority. [ABSTRACT FROM AUTHOR]

Open Access has emerged as an important movement worldwide during the last decade. There are several initiatives now that persuade researchers to publish in open access journals and to archive their pre- or post-print versions of papers in repositories. Institutions and funding agencies are also promoting ways to make research outputs available as open access. This paper looks at open access levels and patterns in research output from India by computationally analyzing research publication data obtained from Web of Science for India for the last 5 years (2014–2018). The corresponding data from other connected platforms—Unpaywall and Sci-Hub—are also obtained and analyzed. The results obtained show that about 24% of research output from India, during last 5 years, is available in legal forms of open access as compared to world average of about 30%. More articles are available in gold open access as compared to green and bronze. On the contrary, more than 90% of the research output from India is available for free download in Sci-Hub. We also found disciplinary differentiation in open access, but surprisingly these patterns are different for gold–green and black open access forms. Sci-Hub appears to be complementing the legal gold–green open access for less covered disciplines in them. The central institutional repositories in India are found to have low volume of research papers deposited. [ABSTRACT FROM AUTHOR]

Salient object detection is a challenging research area and various methods have been proposed in literature. However, these methods usually focus on detecting salient objects in particular type of images only and fail when exposed to a variety of images. Here, we address this problem by proposing a novel framework called Saliency Boosting for refining saliency maps. In particular, the framework trains an Artificial Neural Network Regressor to refine initial saliency measures which are obtained from existing saliency methods. Extensive experiments on seven publicly available datasets viz. MSRA10K-test, DUT-OMRON-test, ECSSD, PASCAL-S, SED2, THUR15K, and HKU-IS have been performed to determine the effectiveness of the proposed framework. The performance of the proposed framework is measured in terms of Precision, Recall, F-Measure, Precision–Recall curve, Overlapping Ratio, Area Under the Curve and Receiver Operating Characteristic curve. The proposed framework is compared with 20 state-of-the-art-methods including best performing methods in the last decade. Further, performance of the proposed framework is better than each individual saliency detection method used in the framework. The proposed framework outperforms or is comparable with 20 state-of-the-art methods in terms of the aforementioned performance measures on all datasets. [ABSTRACT FROM AUTHOR]

Higher participation of women in higher education and research is a very important development goal in many countries across the world, with several countries creating special initiatives and schemes to increase participation of women in higher education and research. This article looks at a case study from India and aims to characterize the participation of women in research, by analysing the parameters of institution-type, discipline, citation impact and international collaboration. Research publication data from 50 most productive Indian institutions, along with data for 5 major institution systems, for a period of 10 years (2008–2017), as indexed in Web of Science, is obtained as sample data and analysed. Results obtained show that participation of women is found to vary in different disciplines, with biology (37%), agriculture science (32%), social science (31%) and medical science (32%) having relatively higher number of female 1st authored papers as compared to engineering (20%), information science (21%) and mathematics (22%). It is also observed that institutions specializing in medical sciences and social science have relatively better participation of women. In terms of location of institution in a big metropolitan city or an urban area, it was found that there do not exist any significant differentiation in levels of participation of women in research between institutions located in bigger cities or smaller towns. Further, analysis of citation and collaboration patterns show that though male authored papers have an edge in citation impact, women researchers get more internationally collaborated papers. [ABSTRACT FROM AUTHOR]

Phase change materials (PCM) have a potential role in thermal energy storage applications. Recent progress has shown notable work on solid solid phase change materials (SS-PCM) which possess unique advantages of low subcooling, limited volume expansion due to a solid solid phase transition, high thermal stability and also had significant latent heat and thermal conductivity values. This review addresses current research on SS-PCM with the focus on solid solid phase transition mechanisms, properties, thermo-physical property enhancement, and experimental case studies for specific energy storage applications. The mechanism of solid solid phase transition for different classifications of SS-PCM with regards to a phase transition temperature and latent heat has provided in detail. This review also includes information regarding fundamental properties of SS-PCM along with methods for property enhancement, details on binary/ternary mixtures of SS-PCM and hybrid SS-PCMs, which allow altering phase transition temperature and increase thermal conductivity. Finally, the potential use of enhanced SS-PCM are discussed in detail, guiding researchers to select, customize, future scope, and addresses research gap of the SS-PCM for different energy storage applications. [ABSTRACT FROM AUTHOR]

Obtaining HOMFLY-PT polynomials H R 1 , ... , R l for arbitrary links with l components colored by arbitrary SU(N) representations R 1 , ... , R l is a very complicated problem. For a class of rank r symmetric representations, the [r]-colored HOMFLY-PT polynomial H [ r 1 ] , ... , [ r l ] evaluation becomes simpler, but the general answer lies far beyond our current capabilities. To simplify the situation even more, one can consider links that can be realized as a 3-strand closed braid. Recently (Itoyama et al. in Int J Mod Phys A28:1340009, 2013. arXiv:1209.6304), it was shown that H [ r ] for knots realized by 3-strand braids can be constructed using the quantum Racah coefficients (6j-symbols) of U q (s l 2) , which makes easy not only to evaluate such invariants, but also to construct analytical formulas for H [ r ] of various families of 3-strand knots. In this paper, we generalize this approach to links whose components carry arbitrary symmetric representations. We illustrate the technique by evaluating multi-colored link polynomials H [ r 1 ] , [ r 2 ] for the two-component link L7a3 whose components carry [ r 1 ] and [ r 2 ] colors. Using our results for exclusive Racah matrices, it is possible to calculate symmetric-colored HOMFLY-PT polynomials of links for the so-called one-looped links, which are obtained from arborescent links by adding a loop. This is a huge class of links that contains the entire Rolfsen table, all 3-strand links, all arborescent links, and, for example, all mutant knots with 11 intersections. [ABSTRACT FROM AUTHOR]

The inherent stringent energy and bandwidth constraints complicate the ascertainment of the reasons for the failure in wireless sensor networks (WSNs). This work proposes a scheme to monitor the networks health, based on node loss rate in a randomly deployed WSN. Considering the node loss inference relies on the knowledge stored at the sink, which is limited to the information of the nodes reporting the data in each round, we first propose a new improved data gathering strategy based on which a inference algorithm is proposed. The proposed algorithm passively tracks the health of the entire network with the help of beacon packets from every node and uses network inference techniques not only to calculate the per node loss rate, but also to precisely identify the faulty nodes in the data transmission path and identify the critical areas in the monitoring field. Results prove the efficacy of the proposed algorithm under two loss scenarios, namely equal loss and cascaded loss. [ABSTRACT FROM AUTHOR]