Labour and Labour Crisis in Nigerian: The Implication of the Structural Adjustment Programme

This Paper Labour and Labour Crises in Nigeria: The implication of structural adjustment programme, its aims at analyzing the effects of the structural adjustment programme (SAP) on the Labour and Labour crises in Nigeria. Through the economic policy programme, which among other objectives of the programmes includes, the privatization of public investment, retrenchment of Staffs, devaluation of the naira, deregulation of interest rates, reduction on government expenditure and Labour market adjustment. This paper also examine the Nigerian economy before the introduction of the Structural adjustment programme (SAP) which also give an insight about the Nigerian drawing rights from 1970 where the 90 percent of the nation foreign exchange earnings and more than 80 percent of the country’s population (53 million) were gaily employed by the agricultural sector. However, with discovery of Oil in the 70s Agricultural contribution to GDP started to decline from about 40 percent, GDP in the early 1970s to 20 percentage in the early 1980s. This decline resulted into untold sequences such as increased dependence on food imports. Now the government source of income solidly depend on Oil with 90 percent of the country exports earnings. As such revenue fell from $27.4 billion in 1950 to$ 11.1 billion in 1982. In the same vein, external reserve dropped from $757 billion to$5.5 billion. These effects resulted into external debt and massive borrowing. In order to find a solution to the problems the government decided to adopt the Structural adjustment programme so that it will dealth with the economic problems as at that time. This also brought about the idea of shifting the resources from undesired sector to more desire ones. Labour in particular is required to shift accordingly from Urban to Rural areas. Government intends to pursue this by applying relative price instrument like devaluation, freeing of urban wages and removal of subsidy. But unfortunately, this result was could not be achieved because of implicit model in pursuing the said objective is defective in many ways. The consequence of these leads to retrenchment of workers where this is not possible, workers were denied their basic pay for months, the Nigeria wage earner was left to fend foe his survival in order to sustain his life and those of his dependents. As such this leads to strike and counter strike which the Labour Union sought to be the only means of obtaining Union rights. Even though this was not effective, since the industries can not affords the exchange rate for their raw materials etc

Failure Prediction using Machine Learning in a Virtualised HPC System and application

YesFailure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular check-pointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the Support Vector Machine (SVM), Random Forest(RF), k-Nearest Neighbors (KNN), Classi cation and Regression Trees (CART) and Linear Discriminant Analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This f inding implies that our method can effectively predict all possible future system and application failures within the system.Petroleum Technology Development Fund (PTDF) funding support under the OSS scheme with grant number (PTDF/E/OSS/PHD/MB/651/14

Optimising Fault Tolerance in Real-time Cloud Computing IaaS Environment

YesFault tolerance is the ability of a system to respond swiftly to an unexpected failure. Failures in a cloud computing environment are normal rather than exceptional, but fault detection and system recovery in a real time cloud system is a crucial issue. To deal with this problem and to minimize the risk of failure, an optimal fault tolerance mechanism was introduced where fault tolerance was achieved using the combination of the Cloud Master, Compute nodes, Cloud load balancer, Selection mechanism and Cloud Fault handler. In this paper, we proposed an optimized fault tolerance approach where a model is designed to tolerate faults based on the reliability of each compute node (virtual machine) and can be replaced if the performance is not optimal. Preliminary test of our algorithm indicates that the rate of increase in pass rate exceeds the decrease in failure rate and it also considers forward and backward recovery using diverse software tools. Our results obtained are demonstrated through experimental validation thereby laying a foundation for a fully fault tolerant IaaS Cloud environment, which suggests a good performance of our model compared to current existing approaches.Petroleum Technology Development Fund (PTDF

A synopsis on the effects of anthropogenic greenhouse gases emissions from power generation and energy consumption

Despite the looming difficult energy context in the majority of countries in the world, global change in environmental dignity resulting from power generation and energy consumption scenario is rapidly becoming a globally disturbing phenomenon. Stakeholders and environmental activists alike have been clamouring for adoption of reduction procedures using sustainable means because ignominious environmental practices have associated disastrous consequences. Increasing essential strategies are needed to fortify the pursuit for the reduction in the emissions from power generation and energy consumption. Therefore, this article presents an overview of the effects of anthropogenic energy generation and consumption practices capable of ejecting emissions of greenhouse gases into the atmosphere. It also endeavors to identify some greenhouse gas emission reduction and control measures

An approach to failure prediction in a cloud based environment

yesFailure in a cloud system is defined as an even that occurs when the delivered service deviates from the correct intended behavior. As the cloud computing systems continue to grow in scale and complexity, there is an urgent need for cloud service providers (CSP) to guarantee a reliable on-demand resource to their customers in the presence of faults thereby fulfilling their service level agreement (SLA). Component failures in cloud systems are very familiar phenomena. However, large cloud service providers’ data centers should be designed to provide a certain level of availability to the business system. Infrastructure-as-a-service (Iaas) cloud delivery model presents computational resources (CPU and memory), storage resources and networking capacity that ensures high availability in the presence of such failures. The data in-production-faults recorded within a 2 years period has been studied and analyzed from the National Energy Research Scientific computing center (NERSC). Using the real-time data collected from the Computer Failure Data Repository (CFDR), this paper presents the performance of two machine learning (ML) algorithms, Linear Regression (LR) Model and Support Vector Machine (SVM) with a Linear Gaussian kernel for predicting hardware failures in a real-time cloud environment to improve system availability. The performance of the two algorithms have been rigorously evaluated using K-folds cross-validation technique. Furthermore, steps and procedure for future studies has been presented. This research will aid computer hardware companies and cloud service providers (CSP) in designing a reliable fault-tolerant system by providing a better device selection, thereby improving system availability and minimizing unscheduled system downtime

Failure Analysis Modelling in an Infrastructure as a Service (Iaas) Environment

yesFailure Prediction has long known to be a challenging problem. With the evolving trend of technology and growing complexity of high-performance cloud data centre infrastructure, focusing on failure becomes very vital particularly when designing systems for the next generation. The traditional runtime fault-tolerance (FT) techniques such as data replication and periodic check-pointing are not very effective to handle the current state of the art emerging computing systems. This has necessitated the urgent need for a robust system with an in-depth understanding of system and component failures as well as the ability to predict accurate potential future system failures. In this paper, we studied data in-production-faults recorded within a five years period from the National Energy Research Scientific computing centre (NERSC). Using the data collected from the Computer Failure Data Repository (CFDR), we developed an effective failure prediction model focusing on high-performance cloud data centre infrastructure. Using the Auto-Regressive Moving Average (ARMA), our model was able to predict potential future failures in the system. Our results also show a failure prediction accuracy of 95%, which is good

Molecular epidemiology of camel trypanosomiasis based on ITS1 rDNA and RoTat 1.2 VSG gene in the Sudan

<p>Abstract</p> <p>Background</p> <p>Internal transcribed spacer one (ITS1) of the ribosomal DNA is known to be a suitable target for PCR-based detection of trypanosomes. The analysis of this region provides a multi-species-specific diagnosis by a single PCR. Using ITS1 primer-based PCR, a cross sectional study was carried out in the period from September to November 2009 on samples collected from 687 camels from geographically distinct zones in the Sudan to detect all possible African trypanosomes, which can infect camels.</p> <p>Results</p> <p>The results showed that all PCR-positive camels were infected with a single parasite species; <it>Trypanosoma evansi</it>. The highest prevalence, 57.1% (117/205), was observed in the Butana plains of mid-Eastern Sudan and the lowest, 6.0% (4/67), was in the Umshadeeda eastern part of White Nile State. In another experiment, the RoTat 1.2 gene encoding the variable surface glycoprotein (VSG) of <it>T. evansi </it>was analyzed for its presence or absence by a polymerase chain reaction (PCR) using <it>T. evansi </it>species-specific primers. The study showed that the RoTat 1.2 VSG gene was absent in thirteen out of thirty <it>T. evansi</it>-positive samples.</p> <p>Conclusions</p> <p>It is concluded that camel trypanosomiasis in Sudan is apparently caused by a single parasite species <it>T. evansi </it>and there were no other typanosomes species detected. In addition, the disease is highly prevalent in the country, which strengthens the need to change control policies and institute measures that help prevent the spread of the parasite. To our knowledge, this is the first molecular diagnosis report, which gives a picture of camel trypanosomiasis covering large geographical areas in Sudan.</p

Meningococcus serogroup C clonal complex ST-10217 outbreak in Zamfara State, Northern Nigeria.

After the successful roll out of MenAfriVac, Nigeria has experienced sequential meningitis outbreaks attributed to meningococcus serogroup C (NmC). Zamfara State in North-western Nigeria recently was at the epicentre of the largest NmC outbreak in the 21st Century with 7,140 suspected meningitis cases and 553 deaths reported between December 2016 and May 2017. The overall attack rate was 155 per 100,000 population and children 5-14 years accounted for 47% (3,369/7,140) of suspected cases. The case fatality rate (CFR) among children 5-9 years was 10%, double that reported among adults ≥ 30 years (5%). NmC and pneumococcus accounted for 94% (172/184) and 5% (9/184) of the laboratory-confirmed cases, respectively. The sequenced NmC belonged to the ST-10217 clonal complex (CC). All serotyped pneumococci were PCV10 serotypes. The emergence of NmC ST-10217 CC outbreaks threatens the public health gains made by MenAfriVac, which calls for an urgent strategic action against meningitis outbreaks

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Background: Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. // Methods: We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung's disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. // Findings: We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung's disease) from 264 hospitals (89 in high-income countries, 166 in middle-income countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in low-income countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. // Interpretation: Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between low-income, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030