Wandering spleen: A common presentation of an uncommon anomaly

Background: With the advent of real time ultrasonography of the abdomen, the spleen is no longer an inaccessible organ. Wandering spleen is a rare entity with only less than 500 cases reported so far. Method: This case report presents a 16-year- old Nigerian girl admitted in a medical centre but referred for ultrasonography on account of a clinical history of lower abdominal tenderness. Result: Ultrasonography examination revealed that the spleen was not found in its normal anatomical position. However, a well defined acoustic signature of the spleen was seen in the pelvis. Conclusion: Ultrasonography which is far cheaper than magnetic resonance imaging (MRI) and computed tomography (CT) is a valuable diagnostic aid in this conditio

“Slow Response to Climate Change in Nigeria: Need for Urgent and Comprehensive Action”

This paper discussed the state of climate change in Nigeria by considering critically the issues and challenges involved. Serious effort was made to present the reader with empirical evidence on the onset of climate change in Nigeria. Issues such as policy responses, together with challenges such as devastating floods and sea level rise in the coastal south, as well as, incessant droughts and desertification in the Sahelian north were examined. Accordingly, climate change impacts and existing responses to those impacts in Nigeria were thoroughly scrutinized. The implications of not making proper choices on climate change issues and challenges were highlighted. The paper concluded that although some effort has been made to mitigate the impacts of climate change in the country (such as the restriction placed on logging in the Cross River High Forest and nationwide afforestation schemes), such efforts are largely fragmentary and much remains to be done especially in the area of strategic planning and capacity building (for instance, dredging of inland rivers and lakes, construction of sea defenses, etc.) to mitigate climate change and adapt to potential and real-time impacts

Providing Access to Knowledge in Africa: the Need for Capacity Building in Classification, Indexing & Abstracting Skills

The realities of the present era of globalization and information and communication technologies (ICT) culminating in the African Virtual Library and Information Network (AVLIN) have made it expedient that African information professionals should be able to develop, showcase and make accessible African indigenous information to the knowledge world. This literature-based opinion paper has tried to identify with the view of the conference organizers that “Major digital initiatives involving African content are currently being undertaken by non-African organization without widely accepted protocols and agreement”. The paper argues that there is a serious need for a theoretical and policy framework necessary to provide a basis for systematic training of library and information science professionals to place African knowledge on a pedestal that will make it accessible to the world of knowledge. It was found that the library schools in most African universities are ill-equipped to train professionals to handle information in the new digital era. This is exacerbated by the fact that professional associations are not doing enough to retool the existing workforce for the task ahead. The paper recommends, among other things, that much emphasis should be placed on the training of cataloguers and indexers in African research institutions and universities to be able to organize African knowledge and produce information surrogates that will help researchers locate them on the internet

Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system

Fuzzy logic systems have been extensively applied for solving many real world application problems because they are found to be universal approximators and many methods, particularly, gradient descent (GD) methods have been widely adopted for the optimization of fuzzy membership functions. Despite its popularity, GD still suffers some drawbacks in terms of its slow learning and convergence. In this study, the use of decoupled extended Kalman filter (DEKF) to optimize the parameters of an interval type-2 intuitionistic fuzzy logic system of Tagagi-Sugeno-Kang (IT2IFLS-TSK) fuzzy inference is proposed and results compared with IT2IFLS gradient descent learning. The resulting systems are evaluated on a real world dataset from Australia’s electricity market. The IT2IFLS-DEKF is also compared with its type-1 variant and interval type-2 fuzzy logic system (IT2FLS). Analysis of results reveal performance superiority of IT2IFLS trained with DEKF (IT2IFLS-DEKF) over IT2IFLS trained with gradient descent (IT2IFLS-GD). The proposed IT2IFLS-DEKF also outperforms its type-1 variant and IT2FLS on the same learning platform

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

This paper presents an approach to prediction based on a new interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference. The gradient descent algorithm (GDA) is used to adapt the parame- ters of the IT2IFLS. The empirical comparison is made on the designed system using two synthetic datasets. Analysis of our results reveal that the presence of additional degrees of freedom in terms of non-membership functions and hesitation indexes in IT2IFLS tend to reduce the root mean square error (RMSE) of the system compared to a type-1 fuzzy logic approach and some interval type-2 fuzzy systems

Machine learning and statistical approaches to classification – a case study

The advent of information technology has led to the proliferation of data in disparate databases. Organisations have become data rich but knowledge poor. Users need efficient analysis tools to help them understand their data, predict future trends and relationships and generalise to new situations in order to make proactive knowledge-driven decisions in a competitive business world. Thus, there is an urgent need for techniques and tools that intelligently and automatically transform these data into useful information and knowledge for effective decision making. Data mining is considered to be the most appropriate technology for addressing this need. Datamining is the process of extracting or “mining” knowledge from large amounts of data. Regression analysis and classification are two datamining tasks used to predict future trends. In this study, we investigate the behaviour of a statistical model and three machine learning models (artificial neural network, decision tree and support vector machine) on a large electricity dataset. We evaluate their predictive abilities based on this dataset. Results show that machine learning models, for this real world dataset, outperform statistical regression while artificial neural network outperforms support vector machine and decision tree in the classification task. In terms of comprehensibility, decision tree is the best choice. Although not definitive this research indicates that certainly these machine learning methods are an alternative to regression with certain datasets

Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems

This paper presents a novel application of a hybrid learning approach to the optimisation of membership and non-membership functions of a newly developed interval type-2 intuitionistic fuzzy logic system (IT2 IFLS) of a Takagi-Sugeno-Kang (TSK) fuzzy inference system with neural network learning capability. The hybrid algorithms consisting of decou- pled extended Kalman filter (DEKF) and gradient descent (GD) are used to tune the parameters of the IT2 IFLS for the first time. The DEKF is used to tune the consequent parameters in the forward pass while the GD method is used to tune the antecedents parts during the backward pass of the hybrid learning. The hybrid algorithm is described and evaluated, prediction and identification results together with the runtime are compared with similar existing studies in the literature. Performance comparison is made between the proposed hybrid learning model of IT2 IFLS, a TSK-type-1 intuitionistic fuzzy logic system (IFLS-TSK) and a TSK-type interval type-2 fuzzy logic system (IT2 FLS-TSK) on two instances of the datasets under investigation. The empirical comparison is made on the designed systems using three artificially generated datasets and three real world datasets. Analysis of results reveal that IT2 IFLS outperforms its type-1 variants, IT2 FLS and most of the existing models in the literature. Moreover, the minimal run time of the proposed hybrid learning model for IT2 IFLS also puts this model forward as a good candidate for application in real time systems

Time series forecasting with interval type-2 intuitionistic fuzzy logic systems

Conventional fuzzy time series approaches make use of type-1 or type-2 fuzzy models. Type-1 models with one index (membership grade) cannot fully handle the level of uncertainty inherent in many real world applications. The type-2 models with upper and lower membership functions do handle uncertainties in many applications better than its type-1 counterparts. This study proposes the use of interval type-2 intuitionistic fuzzy logic system of Takagi-Sugeno-Kang (IT2IFLS-TSK) fuzzy inference that utilises more parameters than type-2 fuzzy models in time series forecasting. The IT2IFLS utilises more indexes namely upper and lower non-membership functions. These additional parameters of IT2IFLS serve to refine the fuzzy relationships obtained from type-2 fuzzy models and ultimately improve the forecasting performance. Evaluation is made on the proposed system using three real world benchmark time series problems namely: Santa Fe, tree ring and Canadian lynx datasets. The empirical analyses show improvements of prediction of IT2IFLS over other approaches on these datasets

Does the use of outsiders' fund enhance shareholders' wealth? Evidence from Nigeria

This paper is an attempt to extend the analysis of the links between the firm's financial structure and the objectives of the firm in maximizing shareholders' wealth. In theory, the financial goal of the firm should be shareholders' wealth maximization as reflected in the book value and the market value of the firm's share. However it is a challenge to management in our world of complex economic environments to achieve this objective. It is against this background that this paper empirically examined the impact of outsiders fund on the firms' shareholders wealth maximization objective using three value maximization indicators; net profit margin viz dividends per share and current ratio from 2004 to 2008 in the Nigerian economy. The study reveals that outsider fund has a positive though not significant impact on dividend per share and current ratio though it was negative and significant impact on net profit margin. Therefore, the study recommends the use of outsiders fund in the financial mix of firms as to magnify shareholders' wealth but an optimal level of outsiders' contribution should be sought for by management. This will reduce the possibility of trading on the equity of shareholders which may lead to bankruptcy of the firm