The association between sperm quality and asymptomatic chlamydial infection in infertile men at a private fertility clinic in Nigeria

Background: Association between chlamydia trachomatis infection and male infertility is debated in literature. There is little or no information from Nigeria. The study aimed to determine the prevalence of chlamydial infection and its association with sperm quality parameters among asymptomatic men that present for infertility treatment in a Nigeria facility.Methods: A cross-sectional study conducted at a private assisted conception clinic in Lagos, Nigeria among 138 men seeking infertility care. Seminal fluid analysis and IgG Chlamydial serology were performed for each participant. Data obtained were analysed using SPSS; p was significant at <0.05.Results: Of the 138 men screened, 13.9% were Chlamydia-positive. Twenty-one per cent of clients who tested positive to Chlamydia had predominantly immotile sperm, compared with 10.2% without the infection; 26.3% with non-progressive motility had Chlamydia, compared with 2.8% men who were not infected. These differences were statistically significant (p = 0.001). More of those (57.9%) with Chlamydia (compared to 33.1% without) had significant leukocyte counts (p = 0.037). There were no statistically significant differences in sperm count and percent motility between serologically positive and negative men.Conclusion: Positive Chlamydia serology is associated with non-progressive motility and leukocytospermia in infertile Nigerian men.Keywords: Chlamydia, semen analysis, infertilit

A steepest descent algorithm for the optimal control of a cascaded hydropower system

Optimal power generation along the cascaded Kainji-Jebba hydroelectric power system had been very difficult to achieve. The reservoirs operating heads are being affected by possible variation in impoundments upstream, stochastic factors that are weather-related, availability of the turbo-alternators and power generated at any time. Proposed in this paper, is an algorithm for solving the optimal release of water on the cascaded hydropower system based on steepest descent method. The uniqueness of this work is the conversion of the infinite dimensional control problem to a finite one, the introduction of clever techniques for choosing the steepest descent step size in each iteration and the nonlinear penalty embedded in the procedure. The control algorithm was implemented in an Excel VBA® environment to solve the ormulated Lagrange problem within an accuracy of 0.03%. It is recommended for use in system studies and control design for the optimal power generation in the cascaded hydropower system

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages

AfriQA:Cross-lingual Open-Retrieval Question Answering for African Languages

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology

EBOLA AND LASSA FEVER DATASET

This data set consist of preprocessed clinical features of both Ebola Virus and Lassa Fever features among infected patients. The data set was collected from two medical hospital and the preprocessing stages carefully monitored both semantic and clinical comparison of these features to make sure there is no multicollinearity among the independents variables to map the target variable in order to make a valid and sensitive decisions

Relationship Management and Customer Retention in the Banking Sector: A Case Study of Akure Metropolis, Nigeria: Research Paper

This paper is to study customer relationship management and customer retention in the banking sector. The specific objectives are to: determine the relationship between service quality and customer retention; investigate the relationship between technology adoption and customer retention; access the relationship between complaint handling and customer retention in the banking sector. A descriptive survey research design was adopted through the questionnaire. Customers of three selected banks: Access/Diamond bank, Guarantee trust bank and Zenith bank constituted the study population. The sample size was 300 which were selected using homogeneous purposive sampling. Primary data used for the study were gathered through a structured questionnaire. Data gathered were analyzed using the Pearson product-moment correlation. The result showed that there is the relationship between service quality and customer retention; furthermore, it showed that there is the relationship between technology adoption and customer retention and finally, it showed that there is the relationship between complaint handling and customer retention. Thus, the study concluded that customer relationship management is positively related to customer retention in Akure Metropolis

MasakhaNER 2.0:Africa-centric Transfer Learning for Named Entity Recognition

African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages

AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology