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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.

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.

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.

This study evaluates the utility of the ensemble framework of feature selection and machine learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic condition of southern Peru. A historical landslide inventory and 24 different landslide influencing factors (LIFs) were prepared using remotely sensed and auxiliary datasets. The LIFs were evaluated using multi-collinearity statistics and their relative importance was measured to select the most discriminative LIFs using the ensemble feature selection method, which was developed using Chi-square, gain ratio, and relief-F methods. We evaluated the performance of ten different ML algorithms (linear discriminant analysis, mixture discriminant analysis, bagged cart, boosted logistic regression, k-nearest neighbors, artificial neural network, support vector machine, random forest, rotation forest, and C5.0) using different accuracy statistics (sensitivity, specificity, area under curve (AUC), and overall accuracy (OA)). We used suitable combinations of individual ML models to develop different ensemble ML models and evaluated their performance in LSM. We assessed the impact of LIFs on ML performance. Among all individual ML models, the k-nearest neighbors (sensitivity = 0.72, specificity = 0.82, AUC = 0.86, OA = 78%) and artificial neural network (sensitivity = 0.71, specificity = 0.85, AUC = 0.87, OA = 79%) algorithms showed the best performance using the top five LIFs, while random forest, rotation forest, and C5.0 (sensitivity = 0.76–0.81, specificity = 0.87, AUC = 0.90–0.93, OA = 82–84%) outperformed other models when developed using all twenty-four LIFs. Among ensemble models, the ensemble of k-nearest neighbors and rotation forest, k-nearest neighbors and artificial neural network, and artificial neural network and rotation forest outperformed other models (sensitivity = 0.72–0.73, specificity = 0.83–0.84, AUC = 0.86, OA = 79%) using the top five LIFs. The landslide susceptibility maps derived using these models indicate that ~2–3% and ~10–12% of the total study area fall within the “very high” and “high” susceptibility. The obtained susceptibility maps can be efficiently used to prioritize landslide mitigation activities.

We contribute to the understanding of mechanisms underlying deep-rooted gender norms by exploring the link between the historical availability of arable land and contemporary gender outcomes. We argue that an abundance of arable land in historical times, i.e., pre-industrial period, required more workers in the fields resulting in norms where women worked and contributed from outside the home as well. Consequently, these societies emphasized women’s health due to its positive effect on their productivity in the fields. Moreover, this economic contribution provided women greater bargaining power in the allocation of intrahousehold resources. The historical avail- ability of arable land for a nation is measured as the weighted mean of the shares of its constituent ethnic groups’ ancestral lands suited to cereal agriculture. Consistent with these arguments, we show that countries with more ancestral arable land have higher female labor force participation rates and better health outcomes, measured by maternal mortality ratio and female-male life expectancy gap. We then illustrate the ‘persistence of norm’ mechanism, by showing that ancestral arable land measured at the district level is positively associated with individual-level attitudes regarding women’s participation in the labor market.

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.

The methodologies applicable for the evaluation of periodontal associated diseases are constantly evolving to provide quick, realistic, and scientifically proven results. Trends in the past followed a clinical evaluation of periodontal tissues and radiographic-based reports that formed the foundation for detection of diseases involving the structures supporting the teeth. As the confines and limitations of conventional strategies became obvious over the passage of time, hand in hand variety of techniques have evolved and experimentally justified. These improvisations are based on an improved understanding of the periodontal-pathogenic cascade. Periodontal pathogenesis and a paradigm shift from disease understanding to disease prevention and treatment entail few prerequisites that demand the objectivity of diagnostics procedure that includes sensitivity and specificity along with an explanation of the intensity of the disease, Gingival crevicular fluid an oral bio-fluid resides in the close proximity with gingival tissues have been widely used to understand and differentiate the periodontal health and diseased status. The biomarkers present in the GCF can be a reliable tool to detect the minute changes seen in the disease processes. The GCF consists of various host and bacterial-derived products as well as biomarkers which in turn can be evaluated for the diagnosis, prognosis as well as management of the periodontal disease. Thus, the review aims at describing GCF as a potential oral biofluid helpful in differentiating periodontal health and disease status.

This bibliometric study analyzes 1433 former reviews on Sustainable Development Goals (SDGs) and Sustainability, providing a comprehensive overview of the evolving research landscape in this domain. Notably, we observe a substantial annual growth rate of 74% in publications and a remarkable 171% increase in total citations from 2016 to 2022, reflecting a growing interest in this area. We identify the leading countries and institutions contributing to quantitative reviews on SDGs and Sustainability. SDG 12 (Sustainable Consumption and Production) emerges as the most extensively studied and is highly represented in influential journals like Sustainability and the Journal of Cleaner Production . Across various research fields, SDGs 12 and 11 (Sustainable Cities and Communities) stand out, with SDGs 4 (Quality Education), 5 (Gender Equality), and 15 (Life on Land) showing significance in specific domains. Thematic analysis reveals key topics like environmental protection, circular economy, life cycle assessment, and supply chain management, with strong connections to SDG 12. Further clusters highlight environmental management, renewable energy, and energy policy linked to SDG 7 (Affordable and Clean Energy), along with a smaller cluster focusing on urbanization driven by SDG 11. Network analysis emphasizes the critical roles of SDGs 12 and 9 (Industry Innovation and Infrastructure) in achieving a sustainable future. However, alternative social network indicators highlight the potential influence of SDGs 8 (Decent Work and Economic Growth), 16 (Peace, Justice and Strong Institutions), and 17 (Partnerships for the Goals) on other goals. Intriguingly, mainstream SDG research predominantly focuses on SDGs 3 and 7, presenting challenges due to the volume and complexity of related publications. While SDG 7 could find suitable outlets in leading journals, addressing SDG 3’s (Good Health and Well Being) complexity remains a formidable task. Nevertheless, conducting bibliometric studies on SDGs 3, 7, and 13 (Climate Action) offers promising opportunities in future if the associated challenges are addressed effectively.

The ever-increasing industrial activities over the decades have generated high toxic metal such as chromium (Cr) that hampers the crop productivity. This study evaluated the effect of Cr on two chickpea ( Cicer arietinum L.) varieties, Pusa 2085 and Pusa Green 112, in hydroponic and pot-grown conditions. First, growth parameters (seed germination, seedling growth, and biomass production) and physio-biochemical parameters (oxidative stress and the content of antioxidants and proline) were measured to evaluate the performance of both varieties grown hydroponically for 21 days at concentrations of 0, 30, 60, 90 and 120 μM Cr in the form of potassium dichromate (K 2 Cr 2 O 7 ). In both varieties, significantly deleterious effects on germination and seedling growth parameters were observed at 90 and 120 μM, while growth was stimulated at 30 μM Cr. Significant increases in malondialdehyde and hydrogen peroxide content and electrolyte leakage demonstrated enhanced oxidative injury to seedlings caused by higher concentrations of Cr. Further, increasing concentrations of Cr positively correlated with increased proline content, superoxide dismutase activity, and peroxide content in leaves. There was also an increase in peroxisomal ascorbate peroxidase and catalase in the leaves of both varieties at lower Cr concentrations, whereas a steep decline was recorded at higher Cr concentrations. In the pot experiments conducted over two consecutive years, growth, yield, yield attributes, grain protein, and Cr uptake and accumulation were measured at different Cr concentrations. Pusa Green 112 showed a significant reduction in plant growth, chlorophyll content, grain protein, pod number, and grain yield per plant when compared with Pusa 2085. Overall, our results indicate that Pusa 2085 has a higher Cr tolerance than Pusa Green 112. Therefore, Pusa 2085 could be used to further elucidate the mechanisms of Cr tolerance in plants and in breeding programmes to produce Cr-resistant varieties.

Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R2 = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R2 = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R2 = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R2 and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data.