Econometric modeling of tobacco exports in the milieu of changing global and national policy regimes: repercussions on the Indian tobacco sector

IntroductionTobacco, an important commercial crop, plays a crucial role in farmers' incomes and livelihoods to a sizable population and contributes significant exchange earnings to the Indian economy. Currently, India is the second-largest tobacco producer after China, with a production of 758 million kg (13% of global production) and exports of ~190 million kg of tobacco (9% of global tobacco export volume). However, there are uncertainties surrounding the tobacco sector, such as growing public health and environmental issues associated with tobacco production and consumption and changing national and international tobacco-related policy regimes. In this context, the current study investigates the determinants of tobacco exports and geographical shifts in export destinations over the years.MethodsThe statistical models employed are co-integration, and vector error-correlation models to test the short-run and long-run dynamics relationship between tobacco exports and the explanatory variables, and the Markov chain approach to find out geographical shifts in export destinations.Results and discussionThe econometric model estimated the relationship between the tobacco export volume with domestic production, export price, and global demand for Indian tobacco, and investigated the geographical shift in export destinations of tobacco in the context of changing global and national policy regimes on the sector. The econometric modeling framework confirms that there exists a statistically significant relationship between Indian tobacco export demand, domestic production, export price, and world demand for Indian tobacco. The geographical shift was evident in major export destinations during the post-WHO-FCTC (Framework Convention on Tobacco Control) regime. The model findings direct that India should take advantage of the export price, and global demand for tobacco as India ratified WHO-FCTC; there is no scope for horizontal expansion of the area under tobacco. This modeling framework aids as a tool to direct and explore the possible options with a greater emphasis on export-centric farming system in tobacco production by augmenting crop compliance and quality to meet the standards of international markets

Crystal growth and characterisation of rare earth phosphates

Growth of rare earth phosphates has been carried out from aqueous phosphoric acid solutions. The system K2O - La2O3 - P2O5 - H2O has been studied in detail within a temperature range 150 to 700 degrees C, and a series of hydrous and anhydrous phosphates have been obtained. The influence of several growth parameters like the ratio of the alkali metals to rare earth metals, the concentration of P2O5, H2O, the pH of the growth media, experimental temperature, etc. on the type and quality of the rare earth phosphate obtained has been studied. The growth mechanism of the formation of various hydrous and anhydrous phosphates in the system K2O - La2O3 - P2O5 - H2O has been studied, in order to obtain crystals with a desired morphology and quality, which helps in the in-situ fabrication of the crystals ready for use as solid state laser hosts. LaP5O14 and KLaP4O12 crystals were doped with Nd up to 20%

Evaluation of the Grounding Resistance of Conductors Buried in Steplike Terrain

Availability of land for conventional air-insulated substations is becoming increasingly difficult not only in urban but also in semiurban areas. When the land made available is highly uneven, the associated technoeconomic factors favors the erection of substations on a steplike-formed ground surface and such constructions are in service for more than ten years in some parts of southern India. Noting that the literature on the performance of ground grids in such a construction is rather scarce, the present work was taken up. Evaluation of the performance of earthing elements in steplike ground forms the main goal of the present work. For the numerical evaluation, a suitable boundary-based methodology is employed. This method retains the classical Galerkin approach for the conductors, while the interfaces are replaced by equivalent fictitious surface sources defined over unstructured mesh. Details of the implementation of this numerical method, along with special measures to minimize the computation, are presented. The performance of basic earthing elements, such as the driven rod, counterpoise, and simple grids buried in steplike ground, are analyzed and compared with that for the case with uniform soil surface. It is shown that more than the earthing resistances, the step potentials can get significantly affected

SIRT-3 Modulation by Resveratrol Improves Mitochondrial Oxidative Phosphorylation in Diabetic Heart through Deacetylation of TFAM

Background and Purpose: Mitochondrial dysfunction remains the crucial cause for many heart diseases including diabetic cardiomyopathy (DCM). Sirtuin-3 (SIRT-3) is a protein deacetylase localized in the mitochondria and regulates mitochondrial function. Being a noteworthy mitochondrial protein deacetylase enzyme, the role of SIRT-3 in DCM is yet to be explored. Experimental Approach: Diabetes mellitus (Type-I, T1DM) was induced using streptozotocin (STZ, 50 mg/kg) in male Sprague Dawley (SD) rats. Rats with >200 mg/dL blood glucose levels were then divided randomly into two groups, DIA and DIA + RESV, where vehicle and resveratrol (25 mg/kg/day) were administered orally in both groups, respectively. Cardiac oxidative stress, fibrosis, and mitochondrial parameters were evaluated. H9c2 cells were transfected with SIRT-3 siRNA and shRNA, and ORF plasmid for silencing and overexpression, respectively. Key Results: After eight weeks, diabetic rat heart showed reduced cardiac cell size, increased oxidative stress and reduction of the activities of enzymes involved in mitochondrial oxidative phosphorylation (OXPHOS). There was reduced expression and activity of SIRT-3 and mitochondrial transcription factor (TFAM) in diabetic heart. Reduced SIRT-3 expression is also correlated with increased acetylation, decreased mitochondrial DNA (mtDNA) binding activity of TFAM, and reduced transcription of mitochondrial DNA encoded genes. Administration of resveratrol prevented the decrease in SIRT-3 and TFAM activity, which was corresponding to the reduced acetylation status of TFAM. Silencing SIRT-3 using siRNA in H9C2 cells showed increased acetylation of TFAM. Conclusion and Implications: Together our data shows that resveratrol activates SIRT-3, regulates the acetylation status of TFAM and preserves the mitochondrial function along with cellular size in diabetic rat heart

An optimized clustering approach for automated detection of abnormalities in MRI brain images

The medical image phenomenon may be a developing and progressive field these days. The processing of MRI images differs between parts of this field. This paper presents an economical approach for identifying tumors in brain MRI imageries. The process consists of following steps: the occurrence of image processing using intermediate filters, image enlargement is obtained by calibration chart calculation (histogram measurement); image segmentation is performed by binding. This approach is considered for the incorporation of morphological practices. Finally, the tumor stage is determined by manipulation of the image editing strategy. This research work presents an automatic brain tumor diagnosis method using MR imaging. The target system identifies and classifies the image segmentation using k-values. It also divides part of the plant into blood and bruises

Optimization of RFM for automated breast cancer detection

Recent years have seen an upsurge in the acceptance of illness diagnosis and prediction utilizing ML algorithms. A ML model can be employed in the diagnosis of breast cancer illness. In this research, an effective breast cancer prediction model with grid search approach is provided. Using the random forest approach, grid search is used to find the best n-estimator, which may provide the highest possible accuracy for predicting breast cancer. The accuracy of the suggested model can then be utilised to contrast its effectiveness to that of a standard RFM. The experimental result analysis demonstrates that the optimized model has 97.07 percent accuracy whereas the regular random forest technique has an accuracy of 94.73 percent in breast cancer detection