The adoption of information and communication technologies in the desing sector and their impact on firm performance : evidence from the Dutch Design sector

This paper analyzes processes and effects of ICT enabled innovation in the Dutch design sector. Although the adoption of Information and Communication Technologies (ICT) is considered as vital in the design sector, little is known about whether and how ICTs affect the firm performance of small and medium-sized companies (SMEs) in the industry. In introducing a conceptual distinction between ICT supporting the information processing and communication, the paper first examines the determinants of ICT adoption. Next, we analyze the effects of ICT adoption on product and process innovation as well as on firm performance, focusing on the mediating role of the innovation processes. The analyses rest on survey data of a sample of 189 Dutch companies in the Web, Graphic, and Industrial Design Sector in the Netherlands. The results indicate that information processing role of ICT supports the exploitation and communication role facilitates the exploration in organizational learning. The exploitation enables process innovation while exploration enables product innovation. Lastly, Information processing technologies and product innovation are important determinants of superior firm performance

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

Estimation of stature from hand and foot dimensions in Hausa neonates: A hospital-based study

Stature (body height) is an important and useful anthropometric parameter for identification of an individual. The aim of the study is to find the correlation between Stature (Length) and measurements from hand and foot dimensions (Hand Length, Hand Breadth, Foot Length and FootBreadth). 501randomly selected Hausa neonates (zero to 28 days) were measured for each of the parameters. All the parameters showed to correlate with Stature at p < 0.01. Foot Length showed the highest correlation value of 0.75, while Foot Breadth had the least correlation value of 0.43. Hand Breadth and Hand Length also had correlation values of 0.62 and 0.60 respectively. Multiple linear regression equations were formed for each of the parameters. In conclusion, the study has provided regression equations that can be used to estimate stature in Hausa neonates of Kano State origin.Key words: Stature, Hausa neonates, Hand and Foot dimensions

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

Constraint on the QED Vertex from the Mass Anomalous Dimension γm=1\gamma_m = 1

We discuss the structure of the non-perturbative fermion-boson vertex in quenched QED. We show that it is possible to construct a vertex which not only ensures that the fermion propagator is multiplicatively renormalizable, obeys the appropriate Ward-Takahashi identity, reproduces perturbation theory for weak couplings and guarantees that the critical coupling at which the mass is dynamically generated is gauge independent but also makes sure that the value for the anomalous dimension for the mass function is strictly 1, as Holdom and Mahanta have proposed.Comment: 8 pages, LaTeX, October 199

Progressive resistance training improving gait performance and mobility in acute and chronic stroke patients

Stroke, a neurological disorder, leads to long-term disability thereby greatly affecting gait and mobility. The purpose of the current study was to investigate the effects of progressive resistance training in both acute and chronic stroke patients. A quasi interventional study was designed and 46 stroke patients were recruited through convenience sampling technique. Sample size was calculated using epi-tool. Patients who had cognitive problems, balance impairments and contractures were excluded from the study. The study was conducted from January to June 2018, at Rafsan Rehab & Research Centre, Peshawar. Progressive resistance exercises starting at 50 % of one repetition maximum (RM) were performed three days/week and for a total duration of nine weeks. Blind assessor measured readings at baseline and after nine weeks. Gait dynamic index (GDI), Six-Meter Walk Test (SMWT) and Five Times Sit-to-Stand (FTSTS) tools were used to collect the required data. The data was analysed at baseline and after nine weeks on SPSS-20. After nine weeks of intervention significant improvement was recorded in patients on GDI (p<0.001), SMWT score (P<0.001) and FTSTS (p<0.001). Progressive resistance training improves mobility limitations and gait in both acute and chronic stroke patients.måsjekke

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

A prospective study of maternal outcome of labor and perinatal outcome in premature rupture of membranes

Background: Premature rupture of membranes refers to rupture of fetal membranes prior to the onset of labor. PROM is of two types viz. term PROM and preterm PROM. The incidence of PROM is at an average of 10%. In 70% of the cases, it occurs in pregnancies at term.  Preterm PROM- defined as PROM prior to 37 weeks of gestation- complicates 2% to 4% of all singleton and 7% to 20% of twin pregnancies. It is the leading identifiable cause of premature birth and accounts for approximately 18% to 20% of perinatal deaths. Methods: This study was conducted in the tertiary care teaching institute in the state of Jammu and Kashmir, India. After taking an informed consent, a proforma was filled on subject history, examination and investigations. These subjects were followed until delivery and then neonates were also followed. Results: Incidence of PROM was 8.76% of which 54.7% were primigravida and 45.3% were multigravida. Most of the patients (70.4%) were term, only 29.6% were preterm with gestational age of &lt;37 weeks. Incidence of PPH was 2.8%, puerperal pyrexia occurred in 9.8%. The incidence of RDS in neonates was 8.9% while the incidence of neonatal sepsis was 6.7%. There was 1.1% perinatal mortality. Conclusions: From this study it can be concluded that basic aim of obstetrician should be to identify the risk factors leading to PROM, to treat complications and implement strict aseptic precautions to prevent maternal and neonatal morbidity

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