Understanding Declining Mobility and Interhousehold Transfers Among East African Pasoralists

Social Identity and Manipulative Interhousehold Transfers Among East African Pastoralists

SSRN Electronic Journal ◽

10.2139/ssrn.328660 ◽

2002 ◽

Cited By ~ 1

Author(s):

Marieke E/Els Huysentruyt ◽

Christopher B. Barrett ◽

John G. McPeak

Keyword(s):

Social Identity ◽

East African ◽

Interhousehold Transfers

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Scanning electron microscopy of the integument of schistosoma rodhaini

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014230x ◽

1986 ◽

Vol 44 ◽

pp. 132-133

Author(s):

P. Evers ◽

C. Schutte ◽

C. D. Dettman

Keyword(s):

Electron Microscopy ◽

Scanning Electron Microscopy ◽

Oral Sucker ◽

Adult Male ◽

Dorsal Surface ◽

Ventral Surface ◽

Sensory Receptors ◽

East African ◽

Dorsal Midline ◽

Scanning Electron

S.rodhaini (Brumpt 1931) is a parasite of East African rodents which may possibly hybridize with the human schistosome S. mansoni. The adult male at maturity measures approximately 3mm long and possesses both oral and ventral suckers and a marked gynaecophoric canal. The oral sucker is surrounded by a ring of sensory receptors with a large number of inwardly-pointing spines set into deep sockets occupying the bulk of the ventral surface of the sucker. Numbers of scattered sensory receptors are found on both dorsal and ventral surfaces of the head (Fig. 1) together with two conspicuous rows of receptors situated symmetrically on each side of the midline. One row extends along the dorsal surface of the head midway between the dorsal midline and the lateral margin.

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International Telesupervision: The Clinical Fellowship Experience

Perspectives of the ASHA Special Interest Groups ◽

10.1044/persp2.sig11.73 ◽

2017 ◽

Vol 2 (11) ◽

pp. 73-78

Author(s):

David W. Rule ◽

Lisa N. Kelchner

Keyword(s):

Graduate Students ◽

Clinical Supervision ◽

Rural Areas ◽

The United States ◽

Speech Language Pathology ◽

Access To Services ◽

East African ◽

The World ◽

Language Pathology ◽

National Certification

Telepractice technology allows greater access to speech-language pathology services around the world. These technologies extend beyond evaluation and treatment and are shown to be used effectively in clinical supervision including graduate students and clinical fellows. In fact, a clinical fellow from the United States completed the entire supervised clinical fellowship (CF) year internationally at a rural East African hospital, meeting all requirements for state and national certification by employing telesupervision technology. Thus, telesupervision has the potential to be successfully implemented to address a range of needs including supervisory shortages, health disparities worldwide, and access to services in rural areas where speech-language pathology services are not readily available. The telesupervision experience, potential advantages, implications, and possible limitations are discussed. A brief guide for clinical fellows pursuing telesupervision is also provided.

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THE MARITIME ZONES OF EAST AFRICAN STATES IN THE LAW OF THE SEA: BENEFITS GAINED, OPPORTUNITIES MISSED

African Journal of International and Comparative Law ◽

10.3366/e0954889008000145 ◽

2008 ◽

Vol 16 (2) ◽

pp. 121-150 ◽

Cited By ~ 4

Author(s):

ALDO CHIRCOP ◽

DAVID DZIDZORNU ◽

JOSE GUERREIRO ◽

CATARINA GRILO

Keyword(s):

Law Of The Sea ◽

East African ◽

The Law

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A Review and evaluation of Machine Translation methods for Lumasaaba

Journal of Digital Science ◽

10.33847/2686-8296.2.1_1 ◽

2020 ◽

pp. 3-17

Author(s):

Peter Nabende

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Machine Translation ◽

Language Processing ◽

Research Area ◽

Data Driven ◽

East African ◽

Data Set ◽

African Languages ◽

Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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