orunan bir ormanlık alanın ağaç türlerinin çeşitliliği ve bolluğu: Güney Gine savan bölgesi (Nijerya)

The study was carried out to assess the diversity and abundance of tree species in Federal College of Wildlife Management, New Bussa, Niger State. Twelve rectangular sample plots of 50x50m (3 hectares) were inventoried in the woodland. 273 trees per hectare were recorded, which belonged to 41 species and 18 families. Anogeissus leiocarpus was the most abundant tree species in the study area. The total basal area was 11.42m2 per hectare. Sterculia setigera had the highest mean diameter at breast height at 51.29 cm while the highest mean height of 14.87 m was recorded for Daniellia oliveri. Shannon-Weiner diversity index (H) value was 3.14. Maximum diversity index (Hmax) was estimated to be 5.61, Pielou’s species evenness index (EH) was 0.56 and Margalef’s index of species richness (M) was calculated to be 7.13. There is need to ensure continuous availability of trees in the study area through effective conservation programme.Bu çalışma, Nijer Eyaleti, New Bussa, Federal Yaban Hayatı Yönetimi Koleji'ndeki ağaç türlerinin çeşitliliğini ve bolluğunu değerlendirmek için yapılmıştır. Ormanlık alanda 50x50m (3 hektar) boyutunda on iki dikdörtgen örnek parselin envanteri çıkarılmıştır. Hektar başına, 18 familya ait 41 türe ilişkin 273 ağaç kaydedilmiştir. Anogeissus leiocarpus, çalışma alanında en bol bulunan ağaç türüdür. Toplam taban alanı hektar başına 11.42m2 olarak hesaplanmıştır. Sterculia setigera; 51.29 cm ile en yüksek ortalama göğüs çapına sahip iken, en yüksek ortalama boy 14.87 m ile Daniellia oliveri için kaydedilmiştir. Shannon-Weiner çeşitlilik indeksi (H) değeri 3.14 olarak hesaplandı. Maksimum çeşitlilik indeksi (Hmax) 5.61, Pielou tür düzgünlük indeksi (EH) 0.56 ve Margalef tür zenginliği indeksi (M) 7.13 olarak hesaplanmıştır. Etkili koruma programları ile çalışma alanındaki ağaçların sürekli mevcudiyetinin sağlanmasına ihtiyaç vardır

Effect of Gender on Students’ Academic Performance in Computer Studies in Secondary Schools in New Bussa, Borgu Local Government of Niger State

This research studied the relationship between student’s gender and academic performance in computer science in New Bussa, Borgu local government of Niger state. Questionnaire which consist of 30 multiple-choice items drawn from Senior School Certificate Examination past questions as set by the West Africa Examination Council in 2014 multiple choice past question was used as the research instrument consist. The questionnaire was administered to 275 students from both private and public schools in the study area. The students’ responses were marked and scored, afterward analysed using independent t-test. The results of the study showed that even though the male students had slightly better performance compared to the female students, it was not significant. This better performance was found to be pronounced in the private school which was shown to possess the best male brains found in the study area. Based on the findings of this study, recommendations were made. Parents are encouraged to provide the right education they can afford for their children irrespective of gender. Also, there should be a deliberate Federal Government policy to encourage absorbance of female students into further study in computer science. Furthermore, it was recommended that stake holders in the education industry should make use of these findings and try to research into ways of making gender sensitive policies. Keywords: Effect, Gender, Academic, Performance, Computer Studies, Secondary School

Effects of Covid-19 Outbreaks on Demand for Electronic News in New Bussa, Nigeria

The work investigates how the Covid-19 outbreak affects the demand for electronic news before and during the lockdown in New Bussa, a major town in Niger State, one of Nigeria’s 36 political subdivisions. It also explores the adoption of social media channels in disseminating news related to the pandemic. The questionnaire on Google form was distributed to the residents of the town to which 150 of them responded. The results,among others, show that even though there was a higher demand for news in the thick of the pandemic, it was not significant. This outcome does not align with the previous findings that found demand significantly risen in a similar situation. What is more, this finding brings a rethinking of the classical conditioning theory to the table

A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

There is a wide application of deep learning technique to unimodal medical image analysis with significant classification accuracy performance observed. However, real-world diagnosis of some chronic diseases such as breast cancer often require multimodal data streams with different modalities of visual and textual content. Mammography, magnetic resonance imaging (MRI) and image-guided breast biopsy represent a few of multimodal visual streams considered by physicians in isolating cases of breast cancer. Unfortunately, most studies applying deep learning techniques to solving classification problems in digital breast images have often narrowed their study to unimodal samples. This is understood considering the challenging nature of multimodal image abnormality classification where the fusion of high dimension heterogeneous features learned needs to be projected into a common representation space. This paper presents a novel deep learning approach combining a dual/twin convolutional neural network (TwinCNN) framework to address the challenge of breast cancer image classification from multi-modalities. First, modality-based feature learning was achieved by extracting both low and high levels features using the networks embedded with TwinCNN. Secondly, to address the notorious problem of high dimensionality associated with the extracted features, binary optimization method is adapted to effectively eliminate non-discriminant features in the search space. Furthermore, a novel method for feature fusion is applied to computationally leverage the ground-truth and predicted labels for each sample to enable multimodality classification. To evaluate the proposed method, digital mammography images and digital histopathology breast biopsy samples from benchmark datasets namely MIAS and BreakHis respectively. Experimental results obtained showed that the classification accuracy and area under the curve (AUC) for the single modalities yielded 0.755 and 0.861871 for histology, and 0.791 and 0.638 for mammography. Furthermore, the study investigated classification accuracy resulting from the fused feature method, and the result obtained showed that 0.977, 0.913, and 0.667 for histology, mammography, and multimodality respectively. The findings from the study confirmed that multimodal image classification based on combination of image features and predicted label improves performance. In addition, the contribution of the study shows that feature dimensionality reduction based on binary optimizer supports the elimination of non-discriminant features capable of bottle-necking the classifier

Assessing the Effects of Coronavirus Outbreaks on the Demand for Electronic Health In Nigeria

Electronic health (e-Health) and Mobile health (m-Health) is perceived as opportunity for patients to access their health care providers in the developing countries during coronavirus pandemic as it has been found to contribute tremendously to health care provision in the developed world even before the pandemic. This study attempts to assess how residents of developing countries annexe e-health and m-health during coronavirus outbreak. More specifically, the study analyses the demand for and adoption of electronic health in the face of coronavirus pandemic in Nigeria (a developing country) using Borgu local government, Niger state as case study. It was found that during the outbreak, residents of the local government did not significantly adopt electronic health during the pandemic majorly due to access to community health worker and cost of adopting electronic health facilities. It was recommended that government and relevant health care agencies that deal policy formulation take necessary measure to encourage wider acceptance of electronic health in Nigeria