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The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam, is currently under construction and has been filling at a fast rate without sufficient known analysis on possible impacts on the body of the structure. The filling of GERD not only has an impact on the Blue Nile Basin hydrology, water storage and flow but also poses massive risks in case of collapse. Rosaries Dam located in Sudan at only 116 km downstream of GERD, along with the 20 million Sudanese benefiting from that dam, would be seriously threatened in case of the collapse of GERD. In this study, through the analysis of Sentinal-1 satellite imagery, we show concerning deformation patterns associated with different sections of the GERD’s Main Dam (structure RCC Dam type) and the Saddle Dam (Embankment Dam type). We processed 109 descending mode scenes from Sentinel-1 SAR imagery, from December 2016 to July 2021, using the Differential Synthetic Aperture Radar Interferometry technique to demonstrate the deformation trends of both—the GERD’s Main and Saddle Dams. The time series generated from the analysis clearly indicates different displacement trends at various sections of the GERD as well as the Saddle Dam. Results of the multi-temporal data analysis on and around the project area show inconsistent subsidence at the extremities of the GERD Main Dam, especially the west side of the dam where we recorded varying displacements in the range of 10 mm to 90 mm at the crest of the dam. We conducted the current analysis after masking the images with a coherence value of 0.9 and hence, the subsequent results are extremely reliable and accurate. Further decomposition of the subsiding rate has revealed higher vertical displacement over the west side of the GERD’s Main Dam as compared to the east side. The local geological structures consisting of weak zones under the GERD’s accompanying Saddle Dam adds further instability to its structure. We identified seven critical nodes on the Saddle Dam that match the tectonic faults lying underneath it, and which display a varying degree of vertical displacements. In fact, the nodes located next to each other displayed varying displacement trends: one or more nodes displayed subsidence since 2017 while the other node in the same section displayed uplift. The geological weak zones underneath and the weight of the Saddle Dam itself may somewhat explain this inconsistency and the non-uniform vertical displacements. For the most affected cells, we observed a total displacement value of ~90 mm during the whole study period (~20 mm/year) for the Main Dam while the value of the total displacement for the Saddle dam is ~380 mm during the same period (~85 mm/year). Analysis through CoastSat tool also suggested a non-uniformity in trends of surface water-edge at the two extremities of the Main Dam.

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of remotely sensed and physical process-based numerical modeling (e.g., Global Land Data Assimilation System (GLDAS) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models) data. Interaction between hydrological and ecological indicators including precipitation, evapotranspiration, surface soil moisture and vegetation indices are presented in the global four types of arid and semi-arid areas. The trends followed by precipitation, evapotranspiration and surface soil moisture over the decade are also mapped using harmonic analysis. This study also shows that some hotspots in these global drylands, which exhibit different processes of land cover change, demonstrate strong coherency with noted groundwater variations. Various types of statistical measures are computed using the satellite and model derived values over global arid and semi-arid regions. Comparisons between satellite- (NASA-USDA Surface Soil Moisture and MODIS Evapotranspiration data) and model (FLDAS and GLDAS)-derived values over arid regions (BSh, BSk, BWh and BWk) have shown the over and underestimation with low accuracy. Moreover, general consistency is apparent in most of the regions between GLDAS and FLDAS model, while a strong discrepancy is also observed in some regions, especially appearing in the Nile Basin downstream hyper-arid region. Data-driven modelling approaches are thus used to enhance the models’ performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (ψ), the mean absolute percentage difference (|ψ|)) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R2)).

The announcement of a buyback informs the market about the company’s decision to repurchase its own shares. This announcement highlights the company’s price valuation and the inefficiencies that exist in the market. This study examines the share buyback announcement effect during the COVID-19 period. The study considered the stocks listed in the National Stock Exchange (NSE) that offered share buyback under tender offer mode during the pre-pandemic period between April 2016 and February 2020 and the post-pandemic period between March 2020 and March 2022. 75 firms in the pre-pandemic period and 43 in the post-pandemic period that announced share buyback under the tender offer method were analyzed. The event study methodology using a market model was employed to determine the presence of abnormal returns during the event period, which consisted of –21 days and +21 days. The findings of the study revealed the existence of abnormal returns in and around the announcement date. Besides, statistically significant cumulative abnormal average returns (CAAR) were also found on the event day, i.e., on Day 0. The study found that the impact of buyback announcements on stock returns significantly differed before and after COVID-19 for 10 and 21-day periods, with no significant differences for shorter periods. These insights can help traders and fund managers make informed portfolio adjustments during turbulent market periods surrounding buyback announcements. AcknowledgementThe authors express their sincere gratitude and special thanks to Dr. Krishna T.A., Assistant Professor, Department of Professional Studies, School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bangalore, India, for encouraging, motivating and providing all the required support throughout this empirical investigation and to accomplish this research task.

Pricing of an Initial public offering (IPO) is a complex phenomenon. Price anomalies are commonly observed in IPO markets, especially in emerging markets. Investors perceived underpricing creates undue market momentum during the offer period with an asymmetric effect across different issue sizes. This study examines the determinants of Book Built IPOs underpricing by considering a sample of 180 Book Built IPOs that went public in India between 2011 and 2020. The determinants were verified for differential issue size public offers. Listing day performance was measured using Listing Day-Absolute Return (LD-AR) and Listing Day-Market Adjusted Return (LD-MAR) models. Further, the data obtained was tested for the explanatory capabilities of firm-specific and market momentum factors for underpricing using OLS models. Concerning the differential issue size, the study found a direct relationship between the issue size and underpricing. Dominant underpricing was observed in the case of moderate to large issue size with a linear progressive return, confirming that there was over-optimism on the part of investors. The study's results also revealed that momentum-specific factors have a significant influence along with firm-specific factors such as firm size, cash flows, a subscription rate of QIBs and RIIs in the listing day return, and underpricing.

The United Nations' (UN) Sustainable Development Goals (SDGs), part of Agenda 2030, comprise 17 interconnected goals and 169 actionable targets, providing an effective framework for addressing diverse issues ranging from individual challenges such as poverty, hunger, and health to broader corporate and global challenges like climate change and equality. Among these interconnected SDGs, this dissertation focuses on the role of climate and infrastructure in global and local sustainability. To this end, earth observations have been conducted utilizing data science techniques to advance these SDGs. For this dissertation, the author has conducted earth studies serving the following SDGs:1. SDG 3 (Good Health and Well-being): The influence of atmospheric aerosols on the fatality rate for COVID-19 was studied. Results suggested that atmospheric aerosol optical depth may be a factor in estimating the coronavirus fatality rate, though uncertainties in the analytical results could be attributed to data quality and the absence of other important factors.2. SDG 6 (Clean Water and Sanitation): A groundwater case study was conducted for California, providing insights into future groundwater availability. Forecasts using deep learning suggested continued deficits in coastal and southern hydrological regions, while increased precipitation was forecasted for some inland hydrological regions.3. SDG 9 (Industry, Innovation, and Infrastructure): Displacements observed on the Grand Ethiopian Renaissance Dam (GERD) (Ethiopia) and the Shasta Dam (California, USA) were analyzed using the latest interferometry techniques. Time series analysis revealed different displacement trends at various sections of the GERD and the Saddle Dam, with varying displacement magnitudes observed on the Shasta Dam.4. SDG 11 (Sustainable Cities and Communities) and 13 (Climate Action): A comprehensive analysis of land stability in Alexandria, Egypt, was conducted. A novel approach was developed to update Digital Elevation Models (DEMs) using the Small Baseline Subset method, facilitating the creation of a probabilistic DEM for the year 2040.

Land-cover change is a critical concern due to its climatic, ecological, and socioeconomic consequences. In this study, we used multiple variables including precipitation, vegetation index, surface soil moisture, and evapotranspiration obtained from different satellite sources to study their association with land-cover changes in the Mediterranean region. Both observational and modeling data were used for climatology and correlation analysis. Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Global Land Data Assimilation System (GLDAS) were used to extract surface soil moisture and evapotranspiration data. Intercomparing the results of FLDAS and GLDAS suggested that FLDAS data had better accuracy compared to GLDAS for its better coherence with observational data. Climate Hazards Group Infra-Red Precipitation with Station Data (version 2.0 final) (CHIRPS Pentad) were used to extract precipitation data while Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to extract the vegetation indices used in this study. The land-cover change detection was demonstrated during the 2009–2018 period using MODIS Land-Cover data. Some of the barren and crop lands in Euphrates-Tigris and Algeria have converted to low-vegetated shrublands over the time, while shrublands and barren areas in Egypt’s southwestern Delta region became grasslands. These observations were well explained by changing trends of hydrological variables which showed that precipitation and soil moisture had higher values in the countries located to the east of the Mediterranean region compared to the ones on the west. For evapotranspiration, the countries in the north had lower values except for countries in Europe such as Bosnia, Romania, Slovenia, and countries in Africa such as Egypt and Libya. The enhanced vegetation index appeared to be decreasing from north to south, with countries in the north such as Germany, Romania, and Czechia having higher values, while countries in the south such as Libya, Egypt, and Iraq having lower trends. Time series analysis for selected countries was also done to understand the change in hydrological parameters, including Enhanced Vegetation Index, evapotranspiration, and soil moisture, which showed alternating drop and rise as well as stagnant values for different parameters in each country.

The coronavirus pandemic has not only gripped the scientific community in the search for a vaccine or a cure but also in attempts using statistics and association analysis—to identify environmental factors that increase its potency. A study by Ogen (Sci Total Environ 726:138605, 2020a) explored the possible correlation between coronavirus fatality and high nitrogen dioxide exposure in four European countries—France, Germany, Italy and Spain. Meanwhile, another study showed the importance of nitrogen dioxide along with population density in determining the coronavirus pandemic rate in England. In this follow-up study, Aerosol Optical Depth (AOD) was introduced in conjunction with other variables like nitrogen dioxide and population density for further analysis in fifty-four administrative regions of Germany, Italy and Spain. The AOD values were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites while the nitrogen dioxide data were extracted from TROPOMI (TROPOspheric Monitoring Instrument) sensor onboard the Sentinel-5 Precursor satellite. Regression models, as well as multiple statistical tests were used to evaluate the predictive skill and significance of each variable to the fatality rate. The study was conducted for two periods: (1) pre-exposure period (Dec 1, 2019–Feb 29, 2020); (2) complete exposure period (Dec 1, 2019–Jul 1, 2020). Some of the results pointed towards AOD potentially being a factor in estimating the coronavirus fatality rate. The models performed better using the data collected during the complete exposure period, which showed higher AOD values contributed to an increased significance of AOD in the models. Meanwhile, some uncertainties of the analytical results could be attributed to data quality and the absence of other important factors that determine the coronavirus fatality rate.

This study aimed to characterize the tableting performance of maize and potato starches, when used in combination either as a disintegrant or binder in solid dosage form development.ObjectivesThis study aimed to characterize the tableting performance of maize and potato starches, when used in combination either as a disintegrant or binder in solid dosage form development.Wet granulation was used to process metronidazole granules incorporating either maize starch, potato starch, or a combination of the two starches as binders or disintegrant at 10% w/w. Granule analysis was carried out on the various formulations and subsequently compressed into tablets weighing approximately 500 mg following the addition of extragranular excipients. Tablet properties were assessed after 24 h of storage.Materials and MethodsWet granulation was used to process metronidazole granules incorporating either maize starch, potato starch, or a combination of the two starches as binders or disintegrant at 10% w/w. Granule analysis was carried out on the various formulations and subsequently compressed into tablets weighing approximately 500 mg following the addition of extragranular excipients. Tablet properties were assessed after 24 h of storage.Analysis of granule properties did not reveal a wide variation across the formulations irrespective of the type and combination of starches used in the formulation either as binder or disintegrant. It was observed, however, that there were slight differences in particle size, bulk and tapped densities of granule formulations containing the combined starch as excipients compared to granule formulations containing individual starch as the excipient. Tablets prepared using the combined starches as binder had lower tensile strength and disintegration time compared to other formulations incorporating the individual starches as binders. However, when evaluated as disintegrant, the tablet formulation containing the combined starches produced tablets with relatively lower disintegration time compared to formulations containing the individual starches as disintegrant.ResultsAnalysis of granule properties did not reveal a wide variation across the formulations irrespective of the type and combination of starches used in the formulation either as binder or disintegrant. It was observed, however, that there were slight differences in particle size, bulk and tapped densities of granule formulations containing the combined starch as excipients compared to granule formulations containing individual starch as the excipient. Tablets prepared using the combined starches as binder had lower tensile strength and disintegration time compared to other formulations incorporating the individual starches as binders. However, when evaluated as disintegrant, the tablet formulation containing the combined starches produced tablets with relatively lower disintegration time compared to formulations containing the individual starches as disintegrant.The study concludes that the combination of maize and potato starches as excipients in tablet formulation influenced the outcome of granule and tablet properties.ConclusionThe study concludes that the combination of maize and potato starches as excipients in tablet formulation influenced the outcome of granule and tablet properties.

This study examines the role of digital adoption and price discovery through informal IPO markets in India. Due to price anomalies, sentiments around the IPO listing day are channelised through Informal markets known as grey markets. This study verifies the determinants of grey market IPO prices and their linkages with the formal market IPO under-pricing. Apart from dominant market and firm-specific factors, it examines how digital adoption variables such as digital payment usage and new demat accounts affect the IPO prices in both channels. In the post-pandemic period, there is a surge in the number of IPOs offered and participation of institutions and individuals in IPOs. Grey market allows traders to bid on IPO applications before they are officially listed, helping to assess under-pricing in issue prices, if any. This study uses data of 1,155 IPOs that went public in India between 2016 and 2025. Using OLS models, the study examines the relationships among variables, and findings indicate that both grey market activity and digital adoption directly influence listing day prices, confirming that under-pricing is predictable through informal channels. Investors should consider these factors, in addition to fundamental aspects, when making IPO investment decisions.

This Study is to make an attempt to find that employees are not motivated solely by money and also employee behavior is linked to the attitudes. In 1966, Herzberg interviewed a number of people in different professionals at different levels to find out two factors:- Those factors that motivated them in workplace. and those factors that prevented job dissatisfaction. Within this study we analyzed factors for the cause of satisfaction or dissatisfaction, or which are related to the motivator factors or to the hygiene factors. According to Herzberg, image of the company, work conditions, salary/ pay, supervision, interpersonal relations' are only hygiene factors but these can be motivating factors. The theory however does not much have a say in developing nations where hygiene factors cannot be taken into full consideration. [PUBLICATION ABSTRACT]