Motion analysis of FPSO in multidirectional seas : the West African offshore region

PhD ThesisThe use of experiment remains the most accurate method in the prediction and evaluation of roll damping. Several models ranging from CFD to analytical and empirical techniques and tools have been developed over the years for this purpose. However, the issue of accurately capturing the adherent multilinear behaviour for hulls with sharp edges and bilge keels remains a challenge until date. The elaborate works of Oliveira and Fernandes (Oliveira and Fernandes ,2006,2010,2014) identified and characterized the existence of two regimes using the bilinear model, later modified to the hyperbolic model. Following their work, and identifying this gap, an enhancement in their formation lead to the introduction of a third damping term, which represented the transition between the large angle side and the small angle regions. A modified hyperbolic model has been proposed and tested against existing models with reasonable agreement in terms of regenerating the measured decay. The model’s capture of the transition region was validated using the rigorous procedure of the bilinear methodology. The relative uncertainty associated with the predictive model was evaluated to fall within 3.5% to 5.9% .The decay data were used to modify the regression model of Oliveira and Fernandes and the enhanced model reduced the predictive error in the model parameters from second to first order range. The extracted damping coefficient and model where implemented in a code to study the influence of directionality and spectrum type on the roll motion response of the freefloating unit (typical FPSO) in real sea environment. Interactive contour plot representation was used to capture the sensitivities of spectrum type, directionality and the multidirectional wave streams summation techniques developed prior to roll motion response simulation. A barred region for the number of regular waves to be used was established using the maximum spectra energy density and the estimated significant wave height as the indicators. A 6dof code was developed using simplified methods and techniques. The novel frequency-spectra weighted technique was proposed for the estimation of the excitation force components of the equation of motion in irregular short crested seas from regular wave formulations. The method was validated by running similar scenarios in HydroD and the irregular wave test on scaled model. The roll motion response from the proposed method compared favourably within first order error range against the HydroD simulations and the irregular wave experiment conducted for ii JONSWAP spectrum for the targeted significant wave heights. Similar error margins were also observed for the measured as well as the estimated wave elevations and all other motion modes. The interactive results from the contour plots when translated into roll motion was very evident in the estimated magnitudes in different sea state spectra combinations (type and directions). The use of the suggested spectra form (lognormal or triangular for the swell sea and JONSWAP-Glenn for the wind sea) for the West African region identified variations in the roll response of between 1-23% or more than 5.5o This goes to further show the need to use site or region specific spectrum for the determination of design and operational parameters for offshore structures and associated units and for personnel seakeeping comforts.Niger delta development commission (NDDC) in Nigeri

Using Data Analytics to Derive Business Intelligence: A Case Study

The data revolution experienced in recent times has thrown up new challenges and opportunities for businesses of all sizes in diverse industries. Big data analytics is already at the forefront of innovations to help make meaningful business decisions from the abundance of raw data available today. Business intelligence and analytics has become a huge trend in todays IT world as companies of all sizes are looking to improve their business processes and scale up using data driven solutions. This paper aims to demonstrate the data analytical process of deriving business intelligence via the historical data of a fictional bike share company seeking to find innovative ways to convert their casual riders to annual paying registered members. The dataset used is freely available as Chicago Divvy Bicycle Sharing Data on Kaggle. The authors used the RTidyverse library in RStudio to analyse the data and followed the six data analysis steps of ask, prepare, process, analyse, share, and act to recommend some actionable approaches the company could adopt to convert casual riders to paying annual members. The findings from this research serve as a valuable case example, of a real world deployment of BIA technologies in the industry, and a demonstration of the data analysis cycle for data practitioners, researchers, and other potential users

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

A review on acrylamide in foods: Sources and implications to health

No Abstract

Mining Twitter Data for Business Intelligence Using Naive Bayes Algorithm for Sentiment Analysis

Today social media has grown to be a big player in the way businesses and organizations operate, especially with the coronavirus pandemic increasing the online footprint of organizations. The use of data from social media to drive business intelligence is now of growing interest to both researchers and business owners. Business owners can now utilize platforms like Twitter to learn about their target audience and improve their business processes to meet their growing needs. Twitter makes it easy to see what is going or about to go viral and vital details like why it is going viral and the players behind it. This research aims to help business owners’ especially small and medium enterprises and start-ups gain a competitive advantage in their industry by using the "crowd wisdom" opportunity via social media. The proposed system is based on Twitter and crawls the platform for relevant data, including; locations, trends, and important actors (influencers) within a specified field; the system cleans the data and presents the information in an actionable format. Python was used for Twitter data mining, and sentiment analysis of the tweets was done using Naive Bayes classifiers

Operational failure assessment of Remotely Operated Vehicle (ROV) in harsh offshore environments

For an effective integrity assessment of marine robotic in offshore environments, the elements’ failure characteristics need to be understood. A structured probabilistic methodology is proposed for the operational failure assessment (OFA) characteristics of ROV. The first step is to assess the likely failure mode of the ROV system and its support systems. This captures the interaction and failure induced events during operation. The identified potential failure modes are further developed into logical connectivity based on the cause-effect relationship. The logical framework is modeled using the fault tree analysis technique to predict the ROV operational failure probability in an uncertain harsh environment. The fault tree analysis captured the logical relationship between the primary, intermediate, and top events probability. The importance measure criteria were adopted to identify the most probable events, links, and their importance on the failure propagation. The model was demonstrated with an ROV for deep arctic water subsea operations. The result identified the control system, communication linkages, human factor, among others, as most critical in the ROV operational failure. The methodology’s application provides core information on the Mean time between failure (MTBF) of the ROV system that could aid integrity management and provides a guide on early remedial action against total failure

Congestion Control Mechanism In GSM Telecommunication: A Review

The level of patronage being experienced in Global System for Mobile Communication (GSM) in Nigeria is overwhelming, this is due to the fact that GSM is been used by anyone , anytime and everywhere .This led to a lot of congestion in network resulting to bandwidth degradation and decrease in quality of services. This research has developed a management algorithm for the management of the GSM congestion and evaluated the algorithm based on their performance, Call admission control was combined with other GSM congestion control mechanism to achieve a good and better result. The work integrates all the algorithms together in order to manage the congestion considering all the strengths and constraints of each algorithm

Association between long-term NSAID use and opioid abuse among patients with breast cancer

Background: Improving survival rates among patients with breast cancer has been associated with an increase in the prevalence of co-morbidities like cancer-related pain. Opioids are an important component in the management of pain among these patients. However, the progression from judicious use to abuse defeats the aim of pain control. Non-steroidal anti-inflammatory drugs (NSAIDs) are recommended as the first step in cancer-related pain management. Due to their anti-inflammatory, anti-neoplastic and neuroprotective properties, NSAIDs have been shown to reduce the risk of progression of certain cancers including breast cancers. In this study, we assessed whether an association exists between long-term NSAID use and opioid abuse among breast cancer survivors. We also explored the relationship between long-term NSAID use and inpatient mortality and length of stay (LOS). Methods: Using ICD-9-CM codes, we identified and selected women aged 18 years and older with breast cancer from the National Inpatient Sample. Our primary predictor was a history of long-term NSAID use. Multivariable regression models were employed in assessing the association between long-term NSAID use and opioid abuse, inpatient mortality and LOS. Results: Among 170,644 women with breast cancer, 7,838 (4.6%) reported a history of long-term NSAID use. Patients with a history of long-term NSAID use had lower odds of opioid abuse (adjusted odds ratio (aOR) 0.53; 95% CI [0.32–0.88]), lower in-hospital mortality (aOR 0.52; 95% CI [0.45–0.60]) and shorter LOS (7.12 vs. 8.11 days). Discussion: Further studies are needed to understand the underlying mechanism of the association between long-term NSAID use and opioid abuse

Security Framework for Storage Area Network (SAN)

Over the years, storage network technology has been faced with significant changes and there are recent innovations trying to improve the level of service and reliability in storage area network.  The need for storage of data and information as well as the increase of security awareness in the general population has brought the concept of Storage Area Network to the forefront. Fibre Channel SANs have become the backbone for serving the information needs of enterprise data centers.This paper presents an overview of SAN technology which is implemented using fibre channel technology, securing a Storage Area Network by using best practices in setting up a SAN. New security protocol called DH CHAP (Diffie Hellman challenge handshake   authentication protocol) are implemented to counter  the threats and protect data authenticity, confidentiality and integrity.