Towards improved trauma care outcomes in India : studies of rates, trends and causes of mortality in urban Indian university hospitals

Introduction Injury is a serious threat to global public health. Every six seconds someone in the world dies as a result of injury, adding up to five million people a year. This is more than the number of deaths due to HIV/AIDS, tuberculosis, malaria and maternal deaths combined. Injury is the top killer among the youth (aged 15-29 years), usually male who are physically fit individuals in their economically productive years. About 90% of all injury deaths occur in Low-and-Middle Income Countries (LMICs). Two million lives could be saved annually if the injury mortality rates in LMICs were reduced to the same level as in High-Income Countries (HICs). This would require implementation of robust injury prevention policies and improved post-injury care within hospitals. In India, injury kills one million people every year. More than half of these patients reach hospitals alive. There is a paucity of data on trauma care outcomes of the injured within Indian hospitals. The aim of this thesis was to explore the rates, trends and causes of in-hospital trauma mortality in urban university hospitals in India. Methods Four studies were conducted in urban university hospitals in India. Study I was a retrospective analysis of 24-hour in-hospital trauma mortality using three cohorts of admitted patients (1998, 2002, 2011) at a single hospital. Studies II-IV were prospective analyses of 30- day in-hospital trauma mortality in four hospitals. The variables collected by trained data collectors were mechanism of injury, transfer status, vital signs, injury to arrival time, arrival to investigation time, injury description by clinical, investigation and operative findings. Study IV used Delphi methods to define optimal trauma care within the urban university hospital context and peer review to evaluate each death for preventability. All patients were stratified by injury severity using the Injury Severity Score (ISS) into mild (1-8), moderate (9-15), severe (16- 25), profound (26-75) ISS categories and by time to death into early (within 24 hours), delayed (between 24 hours and 7 days) or late mortality (between 8 and 30 days of in-hospital stay). Results A declining trend of 24-hour in-hospital mortality was observed in an urban Indian university hospital between the years 1998 and 2015 (I,II). The 30-day mortality rate was 21.4% among all trauma patients admitted to the studied hospitals (II). Simple physiological scoring systems using on-admission vital-signs were comparable in performance to more complex anatomical scoring systems in predicting mortality (II,III). All assessed trauma scoring systems predicted 24-hour early mortality better than 30-day late mortality (III). It is likely that 58% of all trauma deaths in studied hospitals were preventable and two-thirds of all deaths in mild or moderately injured patients with an ISS<16. Issues with airway management (14.3%) and resuscitation with haemorrhage control (16.3%) were identified as contributors to early mortality. Traumatic brain injury and burns accounted for the majority of non-preventable deaths (IV). System-related issues were a lack of protocols, lack of adherence to protocols, prehospital delays and delays in imaging (II,IV). Conclusions One in five trauma patients admitted to the urban university hospitals in India dies within 30-days and this rate is at least twice the mortality rate in HIC hospitals (II). The longitudinal trend in early in-hospital mortality shows a decline over 18 years (I,II). More than half of all in-hospital trauma deaths were preventable (IV). The steps towards improved trauma care outcomes are triage using vital signs (II,III), improved airway management, early haemorrhage control and resuscitation, establishing treatment protocols (IV), maintaining a trauma registry (II) and timely delivery of trauma care (II,IV). More research is needed to understand the causes of late mortality in trauma patients (IV)

A Non-adaptive Partial Encryption of Grayscale Images based on Chaos

AbstractResearch papers published in recent times have focused towards different kinds of image encryption techniques. Image encryption based on Chaos became very popular for cryptography since properties of Chaos are related to two basic properties of good cipher-Confusion and Diffusion. In this paper, A Non-adaptive Partial Encryption of Grayscale Images Based on Chaoshas been proposed. In Partial encryption speed and time is the main factor. We decompose the original grayscale image into its corresponding binary eight bit planes then encrypted using couple tent map based pseudorandom binary number generator (PRBNG). The four significant bit planes, determined by 5% level of significance on contribution of a bit-plane in determination of a pixel value, are encrypted using keys which are obtained by applying the recurrence relation of tent map based PRBNG. Then the four insignificant bit planes along with encrypted significant bit planes are combined to form the final cipher image. In order to evaluate performance, the proposed algorithm was measured through a series of tests to measure the security and effectiveness of the proposed algorithm. These tests includes visual test through histogram analysis, measures of central tendency and dispersion, correlation-coefficient analysis, key sensitivity test, key space analysis, information entropy test, Measurement of Encryption Quality – MSE, PSNR, NPCR, UACI. Experimental results show that the new cipher has satisfactory security and efficient

Data protection in Cloud scenarios

We present a brief overview of the main challenges related to data protection that need to be addressed when data are stored, processed, or managed in the cloud. We also discuss emerging approaches and directions to address such challenges

Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning

We study the prospects of Machine Learning algorithms like Gaussian processes (GP) as a tool to reconstruct the Hubble parameter $H(z)$ with two upcoming gravitational wave missions, namely the evolved Laser Interferometer Space Antenna (eLISA) and the Einstein Telescope (ET). We perform non-parametric reconstructions of $H(z)$ with GP using realistically generated catalogues, assuming various background cosmological models, for each mission. We also take into account the effect of early-time and late-time priors separately on the reconstruction, and hence on the Hubble constant ($H_0$). Our analysis reveals that GPs are quite robust in reconstructing the expansion history of the Universe within the observational window of the specific mission under study. We further confirm that both eLISA and ET would be able to constrain $H(z)$ and $H_0$ to a much higher precision than possible today, and also find out their possible role in addressing the Hubble tension for each model, on a case-by-case basis.Comment: 9 pages, 5 sets of figure

Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive Multi-Hop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed

DDoS victim service containment to minimize the internal collateral damages in cloud computing

Recent Distributed Denial of Service (DDoS) attacks on cloud services demonstrate new attack effects, including collateral and economic losses. In this work, we show that DDoS mitigation methods may not provide the expected timely mitigation due to the heavy resource outage created by the attacks. We observe an important Operating System (OS) level internal collateral damage, in which the other critical services are also affected. We formulate the DDoS mitigation problem as an OS level resource management problem. We argue that providing extra resources to the victim's server is only helpful if we can ensure the availability of other services. To achieve these goals, we propose a novel resource containment approach to enforce the victim's resource limits. Our real-time experimental evaluations show that the proposed approach results in reduction in the attack reporting time and victim service downtime by providing isolated and timely resources to ensure availability of other critical services

On the Untraceability of Anonymous RFID Authentication Protocol with Constant Key-Lookup

A*Star SER