Uganda: A country profile

Uganda lies in the heart of Sub-Saharan Africa.It is situated in East Africa and occupies an area of 241,038 sq km (roughly twice the size of the state of Pennsylvania) and its population is about 35,873,253 (CIA World Factbook, 2012).Uganda is bordered by Tanzania and Rwanda to the south, Democratic Republic of Congo to the west, South Sudan to the north, and Kenya to the east.Uganda is a landlocked country and occupies most of the Lake Victoria Basin, which was formed by the geological shifts that created the Rift Valley during the Pleistocene era.Uganda was a British colony and became an independent- sovereign nation in 1962 without a bloody struggle. Several ethnic groups reside in the country i.e. Baganda, Banyankole, Bahima, Bakiga, Bunyoro, Batoro, Basoga, Bagisu, Langi, Acholi, Lugbara, Karamojong and others.English is the official language by virtue of Article 6(1) of the 1995 Constitution and Swahili is also widely spoken especially in the urban areas.Uganda has no State religion.As a country, Uganda has witnessed some positive development in the area of security.The government managed to plant the seeds of peace in the north by defeating the Lord Resistance Army (LRA) led by Joseph Kony

Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset

Epileptic seizure or epilepsy is a chronic neurological disorder that occurs due to brain neurons\u27 abnormal activities and has affected approximately 50 million people worldwide. Epilepsy can affect patients’ health and lead to life-threatening emergencies. Early detection of epilepsy is highly effective in avoiding seizures by intervening in treatment. The electroencephalogram (EEG) signal, which contains valuable information of electrical activity in the brain, is a standard neuroimaging tool used by clinicians to monitor and diagnose epilepsy. Visually inspecting the EEG signal is an expensive, tedious, and error-prone practice. Moreover, the result varies with different neurophysiologists for an identical reading. Thus, automatically classifying epilepsy into different epileptic states with a high accuracy rate is an urgent requirement and has long been investigated. This PhD thesis contributes to the epileptic seizure detection problem using Machine Learning (ML) techniques. Machine learning algorithms have been implemented to automatically classifying epilepsy from EEG data. Imbalance class distribution problems and effective feature extraction from the EEG signals are the two major concerns towards effectively and efficiently applying machine learning algorithms for epilepsy classification. The algorithms exhibit biased results towards the majority class when classes are imbalanced, while effective feature extraction can improve classification performance. In this thesis, we presented three different novel frameworks to effectively classify epileptic states while addressing the above issues. Firstly, a deep neural network-based framework exploring different sampling techniques was proposed where both traditional and state-of-the-art sampling techniques were experimented with and evaluated for their capability of improving the imbalance ratio and classification performance. Secondly, a novel integrated machine learning-based framework was proposed to effectively learn from EEG imbalanced data leveraging the Principal Component Analysis method to extract high- and low-variant principal components, which are empirically customized for the imbalanced data classification. This study showed that principal components associated with low variances can capture implicit patterns of the minority class of a dataset. Next, we proposed a novel framework to effectively classify epilepsy leveraging summary statistics analysis of window-based features of EEG signals. The framework first denoised the signals using power spectrum density analysis and replaced outliers with k-NN imputer. Next, window level features were extracted from statistical, temporal, and spectral domains. Basic summary statistics are then computed from the extracted features to feed into different machine learning classifiers. An optimal set of features are selected leveraging variance thresholding and dropping correlated features before feeding the features for classification. Finally, we applied traditional machine learning classifiers such as Support Vector Machine, Decision Tree, Random Forest, and k-Nearest Neighbors along with Deep Neural Networks to classify epilepsy. We experimented the frameworks with a benchmark dataset through rigorous experimental settings and displayed the effectiveness of the proposed frameworks in terms of accuracy, precision, recall, and F-beta score

The right to development and its corresponding obligations on developing countries

The right to development is a fundamental right, the precondition of liberty, progress, justice and creativity. This right has raised many expectations and controversies over the years. Developing countries claim that the international economic and political order constitutes an obstacle to the enjoyment of the right to development for their citizens. They therefore see a need for action in the international dimension of the right to development.In their view, they are able to provide the necessary basis for the enjoyment of the right to development only if the international order becomes more conducive to the economic development of developing countries.This paper aims to examine the concept of the right to development as a ‘human right’ focusing mainly on the position of developing countries as to whether they have an obligation to work towards the realization and implementation of this right. The paper concludes that the right to development is now recognized as a ‘human right’ like other internationally accepted human rights.Thus, being a right, it entails obligations of some agents in the society, who have the power to deliver the right or adopt policies that have a high likelihood of delivering the right

Galleria mellonella (L.) (Pyralidae) und Spodoptera exigua (Hübner) (Noctuidae): Wirkungsunterschiede zwischen XenTari® (Bacillus thuringiensis aizawai), NeemAzal T/S® und ihren Kombinationen

Both, G. mellonella and S. exigua, are most important pests in tropical countries. G. mellonella has five to six generations per year (Abid et al. 1997; Ali 1996), there, and feeding in bee combs they find, besides wax, residues of honey, insect skin and pollen (Hachiro & Knox 2000). Li et al. (1987) have shown the efficacy of Bacillus thuringiensis aizawai against G. mellonella. It is registered in the EU as Mellonex for its control, but NeemAzal T/S may also be active, and will have some advantages (Leymann et al. 2000, Melathopoulos et al. 2000). Therefore we conducted new studies here, on the results we shall report. S. exigua is an important polyphagous pest of crops in tropical areas (Brown & Dewhurst 1975). By repeated control with synthetic insecticides, especially by illiterate farmers (Armes et al. 1992; Aggarwal et al. 2006a) resistance to a lot of those insecticides has been built up, making plant protection very difficult. Therefore the need is pronounced for microbial and botanical pesticides (Nagarkatti 1982; Rao et al. 1990), which have different modes of action than synthetic insecticides. Aggarwal et al. (2006b) have started to test such ingredients, but the time of observation was too short (3 days), since the effects of Neem products occur later than those of synthetic insecticides (Basedow et al. 2002). So we conducted new, longer lasting experiments (with 5 to 30 days), on which we give a report here. The experiments were conducted during guest stays of the three co-authors (from Mymensingh, Bangladesh, from Nazreth, Ethiopia, and from Khartoum, Sudan) at the Experimental Station of the Institute of Phytopathology and Applied Zoology at Giessen Univerity.Im Labor wurden die Larven mit trockenem Futter versorgt, das vorher für 20 Sek. in die Testlösung getaucht war. Die Testlösungen, auch in der Kontrolle, wurden mit dem anionischen Detergens Triton X 100 versetzt (10%, davon 0.1 ml). XenTari wurde mit 0.5, 1 und 2 mg/l getestet, NeemAzal mit 2, 4 und 8 mg/l. Bei G. mellonella war die höchste korrigierte Mortalität bei XenTari nach 4 Wochen 77%, bei NeemAzal T/S 100%. So wird letzteres empfohlen. Bei Spodoptera exigua im zweiten Larvenstadium bewirkte XenTari nach 3 Wochen eine maximale Wirkung von 95.6%, während NeemAzal T/S bereits nach 7 Tagen 100% erreichte. Im vierten Larvenstadium erreichte NeemAzal TS eine Mortalität von 46%. Wurden aber beide Präparate bei halber Dosis nacheinander appliziert, war die Wirkung nach 5 Tagen 73% (XenTari zuerst) bzw. 98.8% (NeemAzal T/S zuerst). Letztere Kombination wird für den Bauwollanbau in den Tropen empfohlen, um durch deren Zwischenschaltung die Resistenzbildung gegenüber synthetischen Insektiziden zu vermindern

PARIBASAN SEBAGAI INSPIRASI PENCIPTAAN SENI LUKIS

Paribasan adalah salah satu budaya asli Indonesia tepatnya masyarakat jawa yang keberadaanya kini mulai terpinggirkan. Paribasan ini lah yang menjadi gagasan atau ide utama dalam pembuatan karya lukis tugas akhir ini, hal ini dimaksudkan agar masyarakat Indonesia mengingat kembali tentang salah satu budaya aslinya dan tidak mengabaikannya. Dalam karyanya penulis mengangkat tema kondisi sosial yang terjadi di dalam masyarakat saat ini, dimana terjadi pergeseran norma-norma di dalamnya. Hal tersebut menjadi inspirasi sekaligus tantangan bagi penulis untuk menampilkan sebuah karya seni lukis yang bisa mengangkat budaya sekaligus mengungkap pergeseran norma yang terjadi saat ini. Mengekspresikan pemikiran filosofi paribasan kedalam karya lukis merupakan kegiatan positif yang nantinya dapat memberikan manfaat bagi semua pihak. Kata Kunci                              : paribasan, Penciptaan, Seni Lukis  a especially java that the existent has been forgot and this term (paribasan) is the main idea to create this painting for final task. The purposes are that Indonesian recollect and never ignore one of their origin cultures. In the creation the writer adapted social condition of people that is happening nowadays as the theme which there is displacement norms. This became an inspiration and challenge for writer to present a creation of art painting which can promote the culture and reveal the displacement of norms.          Expressing thought in art of painting is a positive activity and it is hoped that It can give advantages. Key word                                 : paribasan, Creating, Paintin

Accuracy assessment of Tri-plane B-mode ultrasound for non-invasive 3D kinematic analysis of knee joints

BACKGROUND Currently the clinical standard for measuring the motion of the bones in knee joints with sufficient precision involves implanting tantalum beads into the bones. These beads appear as high intensity features in radiographs and can be used for precise kinematic measurements. This procedure imposes a strong coupling between accuracy and invasiveness. In this paper, a tri-plane B-mode ultrasound (US) based non-invasive approach is proposed for use in kinematic analysis of knee joints in 3D space. METHODS The 3D analysis is performed using image processing procedures on the 2D US slices. The novelty of the proposed procedure and its applicability to the unconstrained 3D kinematic analysis of knee joints is outlined. An error analysis for establishing the method's feasibility is included for different artificial compositions of a knee joint phantom. Some in-vivo and in-vitro scans are presented to demonstrate that US scans reveal enough anatomical details, which further supports the experimental setup used using knee bone phantoms. RESULTS The error between the displacements measured by the registration of the US image slices and the true displacements of the respective slices measured using the precision mechanical stages on the experimental apparatus is evaluated for translation and rotation in two simulated environments. The mean and standard deviation of errors are shown in tabular form. This method provides an average measurement precision of less than 0.1 mm and 0.1 degrees, respectively. CONCLUSION In this paper, we have presented a novel non-invasive approach to measuring the motion of the bones in a knee using tri-plane B-mode ultrasound and image registration. In our study, the image registration method determines the position of bony landmarks relative to a B-mode ultrasound sensor array with sub-pixel accuracy. The advantages of our proposed system over previous techniques are that it is non-invasive, does not require the use of ionizing radiation and can be used conveniently if miniaturized.This work has been supported by School of Engineering & IT, UNSW Canberra, under Research Publication Fellowship

Vertex Weighted Spectral Clustering

Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the vertex is close to zero compared to the largest Fiedler coefficient of the graph. We propose a vertex-weighted spectral clustering algorithm which incorporates a vector of weights for each vertex of a given graph to form a vertex-weighted graph. The proposed algorithm predicts association of equidistant or nearly equidistant data points from both clusters while the unweighted clustering does not provide association. Finally, we implemented both the unweighted and the vertex-weighted spectral clustering algorithms on several data sets to show that the proposed algorithm works in general