The Analysis of Indo- British -US Relations During the Period 2009-2019

India is a major power in the Asian continent and hegemonic power in the south Asian region. India is a Strong member of International organizations such as SAARC, Commonwealth organization, the shanghai cooperation and BRICS. Manmohan Sing was the 13th prime minister of India. his second term was from 2019 may 22 to 2014 may 26. He is member of the Indian congress party. The leader of congress party was Sonia Gandhi. Narendra Modi was 14th prime minister of India. His first term was from 2014 may 26 to 2019 may 30. Britain and USA are India most friendly nations. This research seeks to comparatively analyze Indo-British- USA relations during 2nd term of Singh prime minister and the 1st term of Modi prime minister. The author evaluates to research objectives. The researcher will conduct research based on dual research objectives. First, studying of foreign policy of PM Singh & Modi. Second, studying of Indo-British-USA relations during the second term of PM Singh & first term of PM Modi. The author evaluates to broad research questions. First, What are the o foreign policies of PM Singh & Modi? . Second, What are the Indo-British-USA relations during the second term of PM Singh & first term of PM Modi? This research is study based an qualitative data. The researcher used to the secondary data for this research. This study utilized the secondary data from libraries and internet. The researcher used Neo classical realism theory for this research. Studies the country foreign policy and decision making process using Neo classical realism Theory. Recent research on India foreign policy has been minimal. This research is important for those studying Indian political and foreign policy. DOI: http://doi.org/10.31357/fhss/vjhss.v07i02.1

The adaptability of empirical equations to calculate potential evapotranspiration and trend analysis of hydroclimatological parameters for agricultural areas in Newfoundland

Calculation of potential evapotranspiration (PET) has been problematic in Newfoundland (NL) due to the lack of measured data. Therefore, PET data obtained from the Pacific Field Corn Association for St John’s, NL was compared against five empirical PET calculation equations (i.e. (i) radiation-based Priestley-Taylor (PT), and Makkink (M), (ii) temperature-based Hargreaves-Samani (HS), and Turc (T), and (iii) location-based Hamon (H)). Evaluation based on the results concluded that the HS equation would be appropriate to calculate PET in NL. Further calibrations and validations were done to modify the HS to better calculate PET for the growing season (May-October) in NL. The modifications improved the Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE) and co-efficient of determination (R2) of the validated data. Trend assessment carried out using Innovative Trend Analysis (ITA) and Mann-Kendal (MK) tests indicated that both methods were in par with each other. Most of the significant positive trends of monthly total precipitation (0.375-2.210 mm/month/year) were available for September and October. Positive trends for minimum and maximum temperatures were found mostly concentrated within August and September with increments ranging from 0.015 to 0.062 ºC/month/year. PET trends of magnitudes up to 0.011 mm/month/year were observed mostly within September and October. Total water balance did not show as many positive trends as other parameters considered. However, the available positive trends (ranging from 0.018 to 0.076 mm/month/year) were also focused mostly within September. As a conclusion, the HS equation with modifications and error margins (where necessary) can be used to calculate PET accurately for the growing season in NL, and positive trends are observed mostly within the later periods of the growing season. The results of this study could be used in consideration of agricultural expansion, selecting cropping systems and water management systems of NL in future

Reconstruction of the Late Holocene climate and environmental history from North Bolgoda Lake, Sri Lanka, using lipid biomarkers and pollen records

The catastrophic impact and unpredictability of the Indian Ocean Monsoon (IOM) over South Asia are evident from devastating floods, mudslides and droughts in one of the most densely populated regions of the globe. However, our understanding as to how the IOM has varied in the past, as well as its impact on local environments, remains limited. This is particularly the case for Sri Lanka, where erosional landscapes have limited the availability of well-stratified, high-resolution terrestrial archives. Here, we present novel data from an undisturbed sediment core retrieved from the coastal Bolgoda Lake. This includes the presentation of a revised Late Holocene age model as well as an innovative combination of pollen, source-specific biomarkers, and compound-specific stable carbon isotopes of n-alkanes to reconstruct the shifts in precipitation, salinity and vegetation cover. Our record documents variable climate between 3000 years and the present, with arid conditions c. 2334 and 2067 cal a bp. This extreme dry period was preceded and followed by more wet conditions. The high-resolution palaeoenvironmental reconstruction fills a major gap in our knowledge on the ramifications of IOM shifts across South Asia and provides insights during a time of major redistribution of dense human settlements across Sri Lanka.Introduction Background, materials and methods - Study area and site - Sampling - Age–depth model - Biomarker analysis - Compound‐specific carbon isotope analysis - Pollen analysis Results - Chronology and climate zones - Biomarker trends and ratios of n‐alkanes - Triterpenols - δ13C isotopes in n‐alkanes - Pollen Discussion - Palaeoenvironmental implications - Mangrove vegetation, palaeosalinity changes and droughts - Palaeoclimate and palaeoenvironmental reconstruction - Zone 1 (2960 to 2390 cal a bp; 385–252 cm) - Zone 2 (2390 to 1800 cal a bp; 252–140 cm) - Zone 3 (1800 to 1318 cal a bp; 140–60 cm) - Zone 4 (1318 cal a bp to present; 60–0 cm) - South Asian comparisons and potential human implications Conclusion

Mapping Paleolacustrine Deposits with a UAV-borne Multispectral Camera: Implications for Future Drone Mapping on Mars

NASA’s Ingenuity Mars Helicopter has marked a new era in planetary exploration by employing unmanned aerial vehicles (UAVs) to enhance our understanding of planetary surfaces. This study evaluates the potential of UAVs for mapping Martian environments, with Lake Natron, Tanzania, serving as an analog for Martian paleolakes. During two field seasons (2023 January and July), we used a Phantom 4 Pro drone equipped with a MicaSense RedEdge-M multispectral camera, supplemented by in situ analysis using a TerraSpec Halo VNIR-SWIR spectrometer, to capture high-resolution imagery and spectral data. Almost all image processing and analysis, except for image mosaic and digital elevation model (DEM) generation, was performed using Python scripting. We benchmarked the onboard image processing capabilities using a Raspberry Pi 5 single-board computer. Processing steps include digital number (DN)-to-radiance conversion, assessment of the best radiance-to-reflectance conversion method, image mosaic creation, DEM generation, calculation of optimal band indices, and selection of the best classification technique. The research underscores Lake Natron’s diverse lithologies as a suitable analog site and demonstrates significant improvements in classification when normalized elevation data are incorporated with spectral index maps through unsupervised classification methods. The study also addresses challenges related to high-resolution image transmission and processing, advocating for advanced techniques such as image compression and low-power computational models. Additionally, it highlights computational and power limitations as key obstacles, suggesting that emerging technologies such as photonic computing and hybrid controllers could provide viable solutions. These findings emphasize the transformative potential of UAVs in planetary exploration while outlining key areas for future research and technological development

Wide & deep learning for predicting relative mineral compositions of sediment cores solely based on XRF scans, a case study from Pleistocene Paleolake Olduvai, Tanzania

This study develops a method to use deep learning models to predict the mineral assemblages and their relative abundances in paleolake cores using high-resolution XRF core scan elemental data and X-ray diffraction (XRD) mineralogical results from the same core taken at coarser resolution. It uses the XRF core scan data along with published mineralogical information from the Olduvai Gorge Coring Project (OGCP) 2014 sediment cores 1A, 2A, and 3A from Paleolake Olduvai, Tanzania. Both regression and classification models were developed using a Keras deep learning framework to assess the predictability of mineral assemblages with their relative abundances (in regression models) or at least the mineral assemblages (in classification models) using XRF core scan data. Models were created using the Sequential class and Functional API with different model architectures. The correlation matrix of element ratios calculated from XRF element intensity records from the cores and XRD-derived mineralogical information was used to select the most useful features to train the models. 1057 training data records were used for the models. Lithological classes were also used for some models using Wide & Deep neural networks since those combine the benefits of memorization and generalization for mineral prediction. The results were validated using 265 validation data records unseen by the model and discuss the accuracy of models using six test records. The optimized Deep Neural Network (DNN) classification model achieved over 86% binary accuracy while the regression models were also able to predict the relative mineral abundances of samples with high accuracies. Overall, the study shows the efficacy of a carefully crafted Deep Learning (DL) model for predicting mineral assemblages and abundances using high-resolution XRF core scan data