No abstract

Crowdfunding is a method of raising funds from a large number of individuals or businesses. Investors can contribute to any project they are interested in and earn if the initiative is successful. Many crowdfunding sites now exist, and they accept large sums of money from investors and contributors and then leave them with bogus promises. Blockchain-based crowdfunding alters the usual approach to company finance. Generally, when people need to acquire funds to start a firm, they must first develop a strategy, statistical surveys, and models, and then offer their ideas to attract people or organisations. Banks, individual investors, and venture capital firms were among the sources of funding. The modern crowdfunding concept is based on three types of on-screen characters: the task initiator who presents the idea or venture to be financed, individuals or investors who invest in the idea, and a platform that connects these two characters to make the venture successful. It can be used to fund a wide range of start-ups and new concepts, such as inventive activities, medical improvements, travel, and social commercial enterprise projects. This work presents a practical implementation of a crowdfunding application that is secured by a lattice-based cryptosystem for encryption of user data and zero-knowledge proof for the identification of application users. Additionally, machine learning has been used for prediction of campaign success for the benefit of fund contributors.

Background:The CDC has termed traumatic brain injury (TBI) as the "silent epidemic" of developed nations. WHO states, by 2020 TBIs will be third largest killer in the developing world. In India Road traffic accidents (RTAs) accounts for 45-60% of TBIs. Karnataka is ranked the fourth highest in the total number of RTAs in the country. Objectives: To study the clinic-epidemiological profile and determinants of outcome of TBI patients. Methodology: A retrospective case series study was conducted in the setting of Neurosurgery department where data from all TBIs admitted from January 2011 to December 2015 were analyzed based on age, sex, GCS score, mechanism and severity of injury, concurrent injuries, CT scan results, type of management and outcome. Results: Among 5092 patients studied, the majority were aged 15 -45years (68%) and males (82.6%). RTAs (74%) were the leading cause and 63% of had mild, 16% had moderate and 21% had severe head injury. 16% of TBIs were associated with other injuries, majority of which include chest and abdomen injuries and lower limb long bone fractures. Skull bone fractures, acute subdural, intarcerebral and extradural hemorrhages were the most common CT findings. 13.2% of the patients were managed surgically. The overall mortality in our series was 15% and the factors associated with increased mortality are elderly age, poor presenting GCS, associated thoraco abdominal injuries, lower limb and pelvic fracture, multiple bleed, diffuse axonal injury, diffuse subarachnoid hemorrhage, bleed involving more than one cranial fosse, occipital bleed and counter-coup injuries. Conclusions: RTAs are the leading cause of TBIs in Karnataka affecting mainly young adult males. Overall mortality of TBI patients was 15%.

Quantification of uncertainty in input parameters to build a robust 3D geological model is an integral and perhaps the most crucial requirement in high-risk exploration areas. This demands more innovative and effective management of uncertainties for optimizing reserve portfolios and better formulation of exploration and exploitation strategies for oil and gas fields. The present area of study pertains to Mumbai/Western Offshore Basin of India. The reservoirs of the study area are challenging due to their high spatio-temporal heterogeneity and discrete fluid distribution. Wells drilled during the field development plan are devoid of hydrocarbon from the lower zone of the reservoir (Middle Eocene/lower Bassein pay) while upper zone (Late Eocene/upper Bassein pay) produced significant amount of gas despite same lithological composition and structural setup, which reduced the utility of pressure-performance based or conventional modeling approach as it couldn't explain the complex geological set up of the deposition. In this background, a thorough evaluation of critical aspect of most complex and anisotropic carbonate reservoir of Bassein Formation of Middle to Late Eocene age has been taken up to delineate the trends of favorable locales in the area. Inputs from micro-facies analysis, fluid transmissibility of Formations and diagenetic imprint analysis were considered to start the present study. An integrated methodology was designed incorporating seismic, well/logs, core samples, sedimentological, bio-stratigraphic & reservoir data to estimate petrophysical properties and necessary modifications in conventional approach were introduced for capturing the reservoir heterogeneity and stochasticity. Hi-frequency digenetic cycle mapping at log scale and pre-stack inversion results (P & S impedance, Vp/Vs ratio) were incorporated to build a robust geo cellular model and characterize the reservoir. Uncertainty analysis presented in this study is mainly focused on structural and petrophysical parameters. The effect of each parameter/factor and their interaction effect (response) with other parameters are analyzed through Optimization Algorithms, to quantify the uncertainties and its impact on reservoir characterization. Sensitivity analysis indicated that Oil Initially In Place (OIIP) exhibits significant sensitivity to effective porosity and water saturation. Therefore, distribution pattern of these uncertainty parameters are derived from Probability Density Function (PDF) and used to restrict the variability of the volumetric estimates to retain the P10/P90 ratios within the acceptable ranges. Quantification of structural parameter was performed using non-linear multiple regression technique, constrained by statistically Maximum Allowable Error (+Standard Deviation). Present analysis enabled us to reduce the uncertainty associated with various reservoir characterization elements. Further, it enhanced robustness of velocity modeling, petrophysical and lithological interpretation through determination of uncertainties with high degree of accuracy and provided their role in estimation of final hydrocarbon-in-place volumes. The parameterization of the uncertainties deliberated could be used as a template in other fields sharing similar structural and depositional characteristics to mitigate the risks associated with Field Development Plan.