Four multiresponsive and self-sustaining metallogels were synthesized by the reaction of the disodium salt of the ligand carboxymethyl-(3,5-di-tert-butyl-2-hydroxy-benzyl)amino acetic acid with ...Cd(II) and Zn(II) halides, which were found to show excellent selectivity for dye adsorption and separation, and one of the gels shows a rare self-healing property.
This survey presents a brief discussion of different aspects of digital image watermarking. Included in the present discussion are these general concepts: major characteristics of digital watermark, ...novel and recent applications of watermarking, different kinds of watermarking techniques and common watermark embedding and extraction process. In addition, recent state-of-art watermarking techniques, potential issues and available solutions are discussed in brief. Further, the performance summary of the various state-of-art watermarking techniques is presented in tabular format. This survey contribution will be useful for the researchers to implement efficient watermarking techniques for secure e-governance applications.
Reactive species produced in the cell during normal cellular metabolism can chemically react with cellular biomolecules such as nucleic acids, proteins, and lipids, thereby causing their oxidative ...modifications leading to alterations in their compositions and potential damage to their cellular activities. Fortunately, cells have evolved several antioxidant defense mechanisms (as metabolites, vitamins, and enzymes) to neutralize or mitigate the harmful effect of reactive species and/or their byproducts. Any perturbation in the balance in the level of antioxidants and the reactive species results in a physiological condition called “oxidative stress.” A catalase is one of the crucial antioxidant enzymes that mitigates oxidative stress to a considerable extent by destroying cellular hydrogen peroxide to produce water and oxygen. Deficiency or malfunction of catalase is postulated to be related to the pathogenesis of many age-associated degenerative diseases like diabetes mellitus, hypertension, anemia, vitiligo, Alzheimer’s disease, Parkinson’s disease, bipolar disorder, cancer, and schizophrenia. Therefore, efforts are being undertaken in many laboratories to explore its use as a potential drug for the treatment of such diseases. This paper describes the direct and indirect involvement of deficiency and/or modification of catalase in the pathogenesis of some important diseases such as diabetes mellitus, Alzheimer’s disease, Parkinson’s disease, vitiligo, and acatalasemia. Details on the efforts exploring the potential treatment of these diseases using a catalase as a protein therapeutic agent have also been described.
•An integrated approach of spectral enhancement techniques and machine learning models for an accurate mapping of spectrally similar rock types.•The support vector machine outperforms other methods ...i.e. random forest and linear discriminant analysis for rock type classification.•SVM appeared to be less sensitive to the number of samples and mislabeling in training datasets as compared to other machine learning models.•The high-resolution lithological map with distinct litho-contacts of amphibolite, metabasalt, and granite is important for gold mineralization studies.
In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector Machine (SVM) outperforms other Machine Learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area . The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization.
Abstract
Background
Rural Indians have higher mortality rates than urban Indians. However, the rural-urban gap in under-five mortality has changed is less researched. This paper aims to assess 1) ...whether the rural-urban gap in under-five mortality has reduced over time 2) Whether rural children are still experiencing a higher likelihood of death after eliminating the role of other socioeconomic factors 3) What factors are responsible for India’s rural-urban gap in under-five mortality.
Methods
We used all rounds for National Family Health Survey data for understanding the trend of rural-urban gap in under-five mortality. Using NFHS-2019-21 data, we carried out a binary logistic regression analysis to examine the factors associated with under-five mortality. Fairlie’s decomposition technique was applied to understand the relative contribution of different covariates to the rural–urban gap in under-five mortality.
Results
India has witnessed a more than 50% reduction in under-five mortality rate between 1992 and 93 and 2019–21. From 1992 to 93 to 2019–21, the annual decrease in rural and urban under-five mortality is 1.6% and 2.7%, respectively. Yet, rural population still contributes a higher proportion of the under-five deaths. The rural-urban gap in under-five mortality has reduced from 44 per thousand live births in 1992–1993 to 30 per thousand in 2004–2005 which further decreased to 14 per thousand in 2019–2021. There is no disadvantage for the rural children due to their place of residence if they belong to economically well-off household or their mothers are educated. It is wealth index rather than place of residence which determines the under-five mortality. Economic (50.82% contribution) and educational differential (28.57% contribution) are the main reasons for rural-urban under-five mortality gaps.
Conclusion
The existing rural-urban gap in under-five mortality suggests that the social and health policies need to be need to reach rural children from poor families and uneducated mothers. This call for attention to ensure that the future programme must emphasize mothers from economically and educationally disadvantaged sections. While there should be more emphasis on equal access to health care facilities by the rural population, there should also be an effort to strengthen the rural economy and quality of education.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PurposeIn recent years, organizational agility (OA) has garnered significant attention from the academic community. Despite a substantial rise in the academic literature on OA, the nuanced ...understanding of OA among academicians, practitioners and policymakers is limited. To address this research gap, the current study attempts to synthesize the academic literature on organizational literature, understand the evolution of OA literature and state the potential research gaps that may open multiple research avenues.Design/methodology/approachThe current study critically evaluates academic literature published in peer-reviewed journals using the bibliometric approach to map the intellectual structure of identified 224 articles on published literature on OA between 2001 and 2022.FindingsThe findings outline OA's evolutionary trend, most prolific authors, journals, affiliations and countries. Further, network analysis is deployed to unearth prominent OA themes. After that, four key themes of OA from each cluster have been identified and evaluated.Research limitations/implicationsThe study is based on the literature drawn from the SCOPUS database. Although the SCOPUS database is one of the largest databases, the authors believe that the SCOPUS does not contain some publications that might have offered some different insights. Secondly, the bibliometric analysis does not offer the opportunity to provide critical insights into published literature, which is one of the main limitations of bibliometric-based studies. However, despite some of these limitations, the authors believe that the study is a useful guide for scholars, practitioners and policymakers who do not have much information related to OA literature.Originality/valueThis article provides a pioneering review of the OA literature using bibliometrics and network analysis. The results and potential directions for further research may assist researchers in increasing the relevance of OA in the current uncertain and ambiguous environment.
The farmer’s adaptation decision to cope with climate change has drawn considerable attention and recognition of the local and global scale’s human-environmental approach. In this paper, we tried to ...understand the human dimension of adaptation decision of farmers in rural India. We analyse the farmer’s perception of climate change and socio-economic determinants of farm household which influence adaption decisions and adaptation strategies choices. We conducted a micro-level assessment of 700 farmers and farm households in seven districts of the Bihar state of northern India. The data is analysed through descriptive statistics and logistic regression. The study finds that 80 per cent of the surveyed farmers perceive and predict climate changes and choose to adopt. This study found that the key socio-economic variables such as the farmer’s age, gender, household size, education level, off-farm income, and farm-size influence farmers’ adaptation decisions. This study will help identify the critical household characteristics that may be integrated into future policy formulation and implementation to be integrated into future policy formulation and a successful adaptation future.
In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for ...identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of 'Inception-v3' network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.
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