BHU Institutional Repository :: Browsing by Author "Sharma, Ajay"

Browsing by Author "Sharma, Ajay"

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Application of remote sensing to study forest fires
(Elsevier, 2022) Payra, Swagata; Sharma, Ajay; Verma, Sunita
Forest fire is one of the most common disaster that takes place in many forest systems throughout the globe. Forest fire has a devastating impact over the environment, landscape, and ecological succession. Every year entire globe witnesses a large number of forest fires. In the foothills of the Indian Himalayan region (Shivalik), periodical spatial and severity of forest fires varies, as fires in these areas are often associated with a large concentration of pine needles on the forest floor. Thereby, forest fire assessment and mapping is very important to minimize the effect and frequency of fire events. Monitoring forest fire over large area has become cost and time effective by using remote sensing imageries (spaceborne or airborne). Remote sensing is one of the most important tools to study and detecting forest fires in cases. Global as well as periodic coverage of remote sensing data have replaced the traditional methods of fire detection to a huge extent. Remote sensing approaches help to analyze wide scenario and factors that affect forest fire. Availability of a wide range of fire detection sensors like MODIS, VIIRS, Sentinel, and so on provide us with plenty of options for mapping forest fire severity and provide mitigation. Various indices like normalized burned ratio, normalized burned thermal ratio, burned area index are most commonly used for forest fire severity mapping. The present chapter provides an overview on advancements in remote sensing techniques which can be used to map the fire incidence. � 2023 Elsevier Ltd. All rights reserved.
Catalyst- and additive-free syntheses of rhodanine and S-alkyl dithiocarbamate derivatives from sulfoxonium ylides
(Royal Society of Chemistry, 2023) Sharma, Ajay; Pandey, Satyendra Kumar
An efficient catalyst- and additive-free facile access to rhodanine and S-alkyl dithiocarbamate derivatives via multi-component reaction of amines, CS2 and ?-ester sulfoxonium ylides in methanol has been described. The new synthetic methods offer excellent synthetic prospects for several functionalized rhodanines and S-alkyl dithiocarbamates with simple operational procedures. � 2023 The Royal Society of Chemistry.
Copper-Catalyzed Chemoselective O-Aroylation of Phenolic Oxime Ethers with Aryl Aldehydes
(John Wiley and Sons Inc, 2021) Kumar, Upendra; Sharma, Ajay; Kumar, Naveen; Kumar Pandey, Satyendra
Copper-catalyzed oxidative O-aroylation of phenolic oxime ethers with a wide range of aromatic aldehydes are described. This approach offers an efficient synthesis of phenolic oxime ether esters in good to excellent yields. � 2021 Wiley-VCH GmbH
Copper-catalyzed chemoselective oxidative o-aroylation of 2-acetylphenols, alkyl salicylates and 1,3-dicarbonyl compounds using styrene derivatives
(Elsevier Ltd, 2021) Kumar, Upendra; Sharma, Ajay; Kumar, Naveen; Pandey, Satyendra Kumar
A novel copper-catalyzed chemoselective oxidative O-aroylation of 2-acetylphenols, alkyl salicylates and 1,3-dicarbonyl compounds with a wide range of styrene derivatives are described. This approach provides an efficient chemoselective preparation of phenol, alkyl salicylate and enol esters in good to excellent yields. This method represents an alternative protocol for the classical esterification reactions. � 2021 Elsevier Ltd
Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images
(Elsevier Ltd, 2022) Sharma, Ajay; Mishra, Pramod Kumar
The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity. � 2022
Deep Learning Approaches for Automated Diagnosis of COVID-19 Using Imbalanced Training CXR Data
(Springer Science and Business Media Deutschland GmbH, 2022) Sharma, Ajay; Mishra, Pramod Kumar
Due to the exponential rise of COVID-19 worldwide, it is important that the artificial intelligence community address to analyze CXR images for early classification of COVID-19 patients. Unfortunately, it is very difficult to collect data in such epidemic situations, which is essential for better training of deep convolutional neural networks. To address the limited dataset challenge, the author makes use of a deep transfer learning approach. The presence of limited number of COVID-19 samples may lead to biased learning due to class imbalance. To resolve class imbalance, we propose a new class weighted loss function that reduces biasness and improves COVID-19 sensitivity. Classification and preprocessing are two concrete components of this study. For classification, we compare five pre-trained deep neural networks architectures i.e. DenseNet169,�InceptionResNetV2, MobileNet, Vgg19 and�NASNetMobile�as a baseline to achieve transfer learning. This study is conducted using two fused datasets where samples are collected from four heterogeneous data resources. Based on number of classes we make four different classification scenarios to compare five baseline architectures in two stages. These scenarios are COVID-19 vs non- COVID-19, COVID-19 vs Pneumonia vs Normal, COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal and COVID19 vs Normal vs Virus + Bacterial pneumonia. The primary goal of this study is to improve COVID-19 sensitivity. Experimental outcomes show that DenseNet169 achieves the highest accuracy and sensitivity for COVID-19 detection with score of 95.04% and 100% for 4-class classification and 99.17% and 100% for 3 class-classification. � 2022, Springer Nature Switzerland AG.
DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images
(Springer Science and Business Media Deutschland GmbH, 2023) Sharma, Ajay; Mishra, Pramod Kumar
Nuclei segmentation has great significance in biomedical applications as the preliminary step for disease diagnosis and treatment analysis. In this study, we propose a model for automated nuclei identification of varying cell shapes and types from microscopy images. Identifying nuclei helps to understand the underlying mechanism of various diseases in their early stages and provides solutions to enable faster cures. The foremost aim of the study is to develop a lightweight model, capable of segmenting varied shapes and sizes. The proposed architecture exploits multi-scale low-level features following dense high-level feature extraction with multi-feature fusion and special skip connections resulting in enhanced learning capability. The multi-scale feature extractor module extracts low-level information which is further processed using attention-based dense connections to extract semantically meaningful information. The special short-skip residual connections replacing long-skip connections reduced the semantic gap between encoder�decoder features. Moreover, the context encoder module extracts higher-level contextual information of different receptive fields using dilated convolutions making the model robust to different shapes and sizes. The higher-level feature maps propagate upward the decoder connections following the shared attention mechanism of an encoder to decoder features to reconstruct a better segmentation map. Moreover, the evaluation scheme following the proposed test-time augmentation operations improved the mean segmentation performance. The experiments on KDSB18, Synthetic cells, Triple-negative breast cancer (TNBC), MoNuSeg, CryoNuSeg, and BUS datasets demonstrate the suitability of the model for the nuclei segmentation tasks. The DRI-UNet model holds good segmentation performance outperforming baseline architecture by 8.12%, 4.71%, 10.19%, 2.46%, 3.14%, 8.91%, and 9.32% on KDSB18, synthetic cells, TNBC, MoNuSeg, CryoNuSeg, CVC-ClinicDB, and BUS datasets, respectively. We further conducted generalization tests of the proposed model for cross-dataset validation, and two independent MIS datasets confirm model effectiveness for nuclei cell and biomedical image segmentation. � 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images
(Springer, 2022) Sharma, Ajay; Mishra, Pramod Kumar
The outbreak of novel coronavirus (COVID-19) disease has infected more than 135.6 million people globally. For its early diagnosis, researchers consider chest X-ray examinations as a standard screening technique in addition to RT-PCR test. Majority of research work till date focused only on application of deep learning approaches that is relevant but lacking in better pre-processing of CXR images. Towards this direction, this study aims to explore cumulative effects of image denoising and enhancement approaches on the performance of deep learning approaches. Regarding pre-processing, suitable methods for X-ray images, Histogram equalization, CLAHE and gamma correction have been tested individually and along with adaptive median filter, median filter, total variation filter and gaussian denoising filters. Proposed study compared eleven combinations in exploration of most coherent approach in greedy manner. For more robust analysis, we compared ten CNN architectures for performance evaluation with and without enhancement approaches. These models are InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, Vgg19, NASNetMobile, ResNet101, DenseNet121, DenseNet169, DenseNet201. These models are trained in 4-way (COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal) and 3-way classification scenario (COVID-19 vs Pneumonia vs Normal) on two benchmark datasets. The proposed methodology determines with TVF + Gamma, models achieve higher classification accuracy and sensitivity. In 4-way classification MobileNet with TVF + Gamma achieves top accuracy of 93.25% with 1.91% improvement in accuracy score, COVID-19 sensitivity of 98.72% and F1-score of 92.14%. In 3-way classification our DenseNet201 with TVF + Gamma gains accuracy of 91.10% with improvement of 1.47%, COVID-19 sensitivity of 100% and F1-score of 91.09%. Proposed study concludes that deep learning modes with gamma correction and TVF + Gamma has superior performance compared to state-of-the-art models. This not only minimizes overlapping between COVID-19 and virus pneumonia but advantageous in time required to converge best possible results. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Influence of meteorological parameters on lightning flashes over Indian region
(Springer, 2023) Yadava, Pramod Kumar; Sharma, Ajay; Payra, Swagata; Mall, R.K.; Verma, Sunita
Abstract: Lightning flashes (LF) and their association with meteorological variables that can influence the occurrence of lightning have been assessed in detail over the Indian domain, i.e., the Convective Available Potential Energy (CAPE), Relative Humidity (RH), Total Column Water Vapour (TCWV), Surface Temperature (ST) and Outgoing Longwave Radiation (OLR). A high-resolution dataset of LF has been retrieved from the Lightning Imaging Sensor (LIS) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The CAPE, TCWV, RH and ST from 1998 to 2013 are retrieved using ERA-Interim monthly/annual climatology�while OLR was retrieved�from NCEP datasets. The seasonal analysis shows that most LF occur during the pre-monsoon period (March, April, and May) (0.40�0.45 flash/km2/day) over northeast region. During the monsoon season (June, July, and August), the LF dominates over northern India (0.35�0.40 flash/km2/day). The seasonal variation of CAPE shows the maximum (1250�2250 J/kg) during pre-monsoon over the coastal area of NE and SE regions. TCWV and RH show the maximum in monsoon season over northeastern part, which is 50�70 kg/m2 and 60�80%, respectively. The�dependence�of LF�on�meteorological parameters�varies�from�region�to�region,�as�is�evident�from�statistical�analysis.�Maximum LF occurred over the NE (0.049 flash/km2/day) region, followed by the EC (0.041 flash/km2/day) and the lowest in the WC (0.027 flash/km2/day) region of India. The LF showed a significant correlation with CAPE over NE and EC of India because of higher humidity content values over the coastal regions, which form graupel through convection. Over the WH, LF and CAPE showed a good correlation (r = 0.94) because of orographic convection processes. Further, TCWV showed significant correlation with LF over WH (0.89) and minimum over WC (0.23) region. Principal component analysis (PCA) shows that lightning is well correlated with CAPE, RH, TCWV and ST over most regions in India. However, lightning is not significantly correlated with OLR. Understanding the meteorology of lightning across the Indian region can inform forecasting of possible lightning events and is relevant for assessing lightning for human, wild risk and climate projections. Research highlights: The regional-scale meteorological variables associated with lightning are identified.Due to the regional orography, lightning flashes show high correlation in proximity to meteorological variables.The spatio-temporal distribution shows that most of the lightning flashes occur during March, April and May (MAM) months over North East region.The impact of meteorological variables is visible as the study's threshold values change over time. � 2023, Indian Academy of Sciences.
Metal-Free Syntheses of ?-Ketothioamide and ?-Ketoamide Derivatives from Sulfoxonium Ylides
(American Chemical Society, 2022) Chaubey, Trayambek Nath; Borpatra, Paran J.; Sharma, Ajay; Pandey, Satyendra Kumar
An efficient base, additive and metal-free synthetic methods for ?-ketothioamide and ?-ketoamide derivatives from readily available sulfoxonium ylides have been described. Sulfoxonium ylides with primary or secondary amines afforded ?-ketothioamides in the presence of elemental sulfur, whereas ?-ketoamides were produced when I2and TBHP were present. The reaction proceeded well at room temperature and generated the corresponding molecules in good to excellent yields. The reaction can be scaled-up and tolerated by a range of functional groups with simple operational procedures. � 2022 American Chemical Society. All rights reserved.
Multicomponent Reaction of CS2, Amines, and Sulfoxonium Ylides in Water: Straightforward Access to ?-Keto Dithiocarbamates, Thiazolidine-2-thiones, and Thiazole-2-thiones
(American Chemical Society, 2023) Kumar, Naveen; Sharma, Ajay; Kumar, Upendra; Pandey, Satyendra Kumar
Simple, versatile, and catalyst-free synthetic methods for ?-keto dithiocarbamates, thiazolidine-2-thiones, and thiazole-2-thiones via the multicomponent reaction of CS2, amines, and sulfoxonium ylides have been described. The ?-keto sulfoxonium ylides furnished ?-keto dithiocarbamates in the presence of CS2 and secondary amines, whereas primary amines afforded thiazolidine-2-thiones or thiazole-2-thiones after dehydration in an acidic environment. With simple procedures, the reaction has a wide substrate scope and excellent functional group tolerance. � 2023 American Chemical Society.
Organocatalyzed epoxidation in the total synthesis of (?)-trans-, (+)-trans- and (+)-cis-disparlures
(Royal Society of Chemistry, 2023) Sharma, Ajay; Pandey, Satyendra Kumar
A simple, flexible and efficient organocatalyzed synthetic approach for the synthesis of (?)-trans-, (+)-trans- and (+)-cis-disparlures has been described. The pivotal reaction sequence comprises organocatalyzed asymmetric J�rgensen epoxidation, Wittig olefination, migration of epoxide and Mitsunobu inversion reaction. Excellent enantiomeric purity (?99%) was achieved during the synthesis of disparlure enantiomers by the J�rgensen epoxidation key step. � 2023 The Royal Society of Chemistry.
Organocatalyzed Protecting-Group-Free Total Synthesis of (S,S)- and (S,R)-Reboxetine, an Antidepressant Drug
(American Chemical Society, 2022) Sharma, Ajay; Pandey, Satyendra Kumar
An efficient, simple, and concise organocatalyzed protecting-group-free synthetic approach to the stereoisomers of the antidepressant drug reboxetine and its implementation toward the asymmetric synthesis of (S,S)-reboxetine and (S,R)-reboxetine from commercially available trans-cinnamaldehyde are described. The synthesis features organocatalytic J�rgensen asymmetric epoxidation, epoxide migration, and Mitsunobu inversion as key steps. � 2022 American Chemical Society. All rights reserved.
Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis
(Springer Science and Business Media B.V., 2022) Sharma, Ajay; Mishra, Pramod Kumar
Healthcare systems around the world are facing huge challenges in responding to trends of the rise of chronic diseases. The objective of our research study is the adaptation of Data Science and its approaches for prediction of various diseases in early stages. In this study we review latest proposed approaches with few limitations and their possible solutions for future work. This study also shows importance of finding significant features that improves results proposed by existing methodologies. This work aimed to build classification models such as Na�ve Bayes, Logistic Regression, k-Nearest neighbor, Support vector machine, Decision tree, Random Forest, Artificial neural network, Adaboost, XGBoost and Gradient boosting. The experimental study chooses group of features by means of three feature selection approaches such as Correlation-based selection, Information Gain based selection and Sequential feature selection. Various Machine learning classifiers are applied on these feature subsets and based on their performance best feature subset is selected. Finally, ensemble based Max Voting Classifier is proposed on top of three best performing models. The proposed model produces an enhanced performance label with accuracy score of 99.41%. � 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.
Performance evaluation of MODIS and VIIRS satellite AOD products over the Indian subcontinent
(Frontiers Media S.A., 2023) Payra, Swagata; Sharma, Ajay; Mishra, Manoj Kumar; Verma, Sunita
In the present study, the first systematic performance evaluation of aerosol optical depth (AOD) products retrieved using two satellite sensors i.e., Visible Infrared Imaging Radiometer Suite (VIIRS) and Aqua-Moderate-Resolution Imaging Spectroradiometer (MODIS) is carried out over India. We have used ground-based AOD from AERONET at 550�nm wavelength for inter-comparison with MODIS Aqua version C6.1 (C061) Deep Blue (DB) aerosol product and VIIRS/SNPP collection version 1.1 (V1.1) DB aerosol product over the time span of 7-year (2014�2020) observation periods. For validation, the average value of satellite pixels falling within the box of 50�Km x 50�Km keeping the AERONET station at the center is retrieved. The average daily data from the AERONET sun photometer (2014�2019) were obtained within �15�min of satellite overpass time. Statistical parameters like correlation coefficient (R), RMSE, MAE, and RMB were calculated. The uncertainty of satellite AOD is evaluated using an envelope of Expected Error (EE = �0.05 + 0.15 AOD for land). Statistical analysis shows that the MODIS AOD product outperforms VIIRS-retrieved AOD. The AOD retrieved from both sensors yields a high correlation (0.86�Jaipur, 0.79�Kanpur, 0.84�Gandhi College, and 0.74�Pune for MODIS and 0.75�Jaipur, 0.77�Kanpur, 0.49�Gandhi College, and 0.86�Pune for VIIRS) and low MAE (0.12�Jaipur, 0.20�Kanpur, 0.15�Gandhi College, and 0.09�Pune for MODIS and 0.13�Jaipur, 0.13�Kanpur, 0.26�Gandhi College, and 0.10�Pune for VIIRS). Other statistical measures such as RMSE, RMB, and P also suggest similar performance. More than 66% of the total data fall within the range of EE for both the satellite products at each station. Spatial comparison exhibits the same AOD pattern seasonally as well as annually having a minimum bias from ?0.3 to +0.3 between MODIS and VIIRS. Slight underestimation and overestimation are observed in all the stations by MODIS, whereas VIIRS continuously underestimates AOD with increase in optical depth, suggesting improvements in the aerosol model and surface reflection in retrieval. Overall, the comparison of ground AERONET AOD reveals better accuracy of MODIS AOD with that of VIIRS satellite datasets over India. Copyright � 2023 Payra, Sharma, Mishra and Verma.
Proline-catalyzed synthesis of ?-substituted (E)-?,?-unsaturated aldehydes from epoxides
(Royal Society of Chemistry, 2023) Sharma, Ajay; Pandey, Satyendra Kumar
A novel, simple and metal-free tandem approach for synthesizing ?-substituted (E)-?,?-unsaturated aldehyde derivatives through acid-catalyzed epoxide rearrangement and organocatalyzed aldol condensation processes has been described. This transformation has a broad substrate scope under mild conditions, including epoxides and aldehydes containing diverse functional groups, resulting in moderate to high yields of the desired products. Eventually, large-scale reactions and the synthesis of some bioactive molecules are used to demonstrate the potential applicability of the developed method. � 2023 The Royal Society of Chemistry
Rapid flash flood calamity in Chamoli, Uttarakhand region during Feb 2021: an analysis based on satellite data
(Springer Science and Business Media B.V., 2022) Verma, Sunita; Sharma, Ajay; Yadava, Pramod Kumar; Gupta, Priyanshu; Singh, Janhavi; Payra, Swagata
The present study investigates the accelerating factors for extreme flash flood at Chamoli district of Uttarakhand on 7 February 2021. The Sentinel-2A and 2B satellite data have been used to depict changes in pre-flood (16th of January) i.e., 5�years of 2016 to 2021 to post-flood (10 February, 2021) situation over the study domain. Vegetation and snow-cover from 2016 to 2021 has been obtained using Normalized Difference Vegetation Index (NDVI) classification over study area. Normalized Difference Water Index (NDWI) is used to extract the pre and post-flood water pixels for flood inundation mapping. The Cartosat-1 digital elevation model (DEM) product is used for drainage pattern and stream order mapping. Correlation between the meteorological parameters such as snowfall, wind speed and wind direction of Nanda Gunti peak during the time of flood with the flood event is analysed. The overall results indicate heavy snowfall (4.22�mm/day) over Nanda Gunti hills followed by high wind speed (23�km/hr.) that might have led to initiation of avalanche/landslide, giving rise to massive flash flood and eroded approximately 0.0263 km3 volume of landmass along with snow cover. Further, the 5�years NDVI analysis shows decrease in vegetation near Rishiganga and Alaknanda, a higher order river streams, is also crucial factor for flood intensification that caused massive destruction within the study area. The work highlights the importance of mapping of intense events and underline factors to reduce the impact and losses in case of future events. � 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Straightforward access to ?-thiocyanoketones and thiazoles from sulfoxonium ylides
(Royal Society of Chemistry, 2023) Sharma, Ajay; Gola, Ajay Kant; Pandey, Satyendra Kumar
Efficient, versatile, and metal-free strategies for synthesizing ?-thiocyanoketones and thiazoles from ?-ketosulfoxonium ylides and ammonium thiocyanate have been described. Due to its simplicity, benign reaction conditions, excellent chemoselectivity, and high yield, this method represents a unique approach for divergent synthesis. Finally, the potential value of the developed methods is demonstrated via large-scale reactions and synthesis of Fanetizole, an anti-inflammatory drug. � 2023 The Royal Society of Chemistry.
Synthesis of ?-Carbonyl-??-amide Sulfoxonium Ylides from Isocyanates with Complete Atom Economy
(American Chemical Society, 2023) Gola, Ajay Kant; Sharma, Ajay; Pandey, Satyendra Kumar
An efficient catalyst- and additive-free facile synthesis of ?-carbonyl-??-amide sulfoxonium ylides from isocyanates and ?-ketosulfoxonium ylides with complete atom economy has been described. The ?-ketosulfoxonium ylides and isocyanates adorned with various functional groups were well-tolerated and afforded moderate to high yields of the ?-carbonyl-??-amide sulfoxonium ylide derivatives. Finally, using large-scale reactions and converting the synthesized ylides into other valuable compounds, we demonstrated the practicality of this synthetic method. � 2023 American Chemical Society.

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