Farmers' knowledge and perception of enset Xanthomonas wilt in southern Ethiopia

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: The dataset supporting the conclusions of this article is included within the article (“Additional file 1 Datasets”).Background: Enset Xanthomonas wilt (EXW) was first reported in 1939 and continues to threaten the sustainability of farming systems in south and southwestern parts of Ethiopia. The present study was conducted in the central zones of southern Ethiopia to assess farmers' knowledge and perception about EXW, its etiology and mode of transmission, and its implications for the management of EXW. Methods: A survey was conducted in 240 households across Hadiya, Kembata-Tembaro and Wolaita zones of southern Ethiopia using focus group discussions and a structured questionnaire to assess farmers' perceptions of causes and modes of EXW transmission, and their knowledge on symptom identification. In addition, EXW prevalence, incidence and severity were determined for each zone. Data were analyzed through descriptive statistics. Results: The results showed that a significant number of farmers are aware of EXW, its symptoms, etiology and transmission and spread, but they are not able to readily relate modes of spread to control methods. Since 2002, EXW became prominent in Hadiya, with the highest EXW incidence and severity, followed by Wolaita, and Kembata-Tembaro. Farmers identified EXW as the major cause for declining production and productivity of enset in the region. Conclusion: EXW has spread widely and rapidly in southern Ethiopia, with significant socioeconomic impacts in smallholders' livelihoods. There is a need for developing knowledge-based strategies and awareness-raising campaign for EXW management.This work was supported by the McKnight foundation, Africa RISING and Ethiopian Biodiversity Institute (EBI)

Morphological Variation and Inter-Relationships of Quantitative Traits in Enset (Ensete ventricosum (welw.) Cheesman) Germplasm from South and South-Western Ethiopia

This is the final version. Available on open access from MDPI via the DOI in this recordEnset (Ensete ventricosum (Welw.) Cheesman) is Ethiopia's most important root crop. A total of 387 accessions collected from nine different regions of Ethiopia were evaluated for 15 quantitative traits at Areka Agricultural Research Centre to determine the extent and pattern of distribution of morphological variation. The variations among the accessions and regions were significant (p ≤ 0.01) for all the 15 traits studied. Mean for plant height, central shoot weight before grating, and fermented squeezed kocho yield per hectare per year showed regional variation along an altitude gradient and across cultural differences related to the origin of the collection. Furthermore, there were significant correlations among most of the characters. This included the correlation among agronomic characteristics of primary interest in enset breeding such as plant height, pseudostem height, and fermented squeezed kocho yield per hectare per year. Altitude of the collection sites also significantly impacted the various characteristics studied. These results reveal the existence of significant phenotypic variations among the 387 accessions as a whole. Regional differentiations were also evident among the accessions. The implication of the current results for plant breeding, germplasm collection, and in situ and ex situ genetic resource conservation are discussed.This study was part of the PhD research work of the first author, and we acknowledge the McKnight Foundation for financial support

IMPACT OF NUMBER OF ATTRIBUTES ON THE ACCURACY OF HUMAN MOTION CLASSIFICATION

The quality of the human motion data faces challenges in producing high classification accuracy in large data streams for essential knowledge discovery. This reflects the need to identify the key factors that affect the results of classification. Present studies merely focus on estimating joints, skeleton and motions of human activities. However, the effect of the number of attributes towards classification accuracies of human motion has not been discussed. Therefore, this paper is aimed at determining the amount of attributes that affect the qualities of human motion classification. The case studies involve simple locomotion activities: jumping, walking and running retrieved from the public available domain. The raw video data were transformed into numeric in the form of x and y-coordinates and rotation angles as to be tested from a single up to triple combinations of data attributes. The impact of the number of attributes on classification accuracy is evaluated via Bayes, Function, Lazy, Meta, Rule and Trees classifier algorithms supported by the WEKA tool. Results revealed that three attributes data gave the best classification performance with an average accuracy of 81.50%. The findings also revealed that the number of attribute is directly proportional to the classification accuracy of human motion data

NUMERICAL STUDY TO PREDICT PROPERTIES OF WAXY CRUDE OIL UNDER DYNAMIC COOLING

In this paper, a parametric study was conducted via ANSYS-Fluent workbench to predict the properties of waxy crude oil subjected to dynamic cooling. Different case studies were conducted to validate the numerical study, and prediction of temperature for pipelines of up to 100 m length was made. It was observed that the percentage difference between the experimental and simulation results was below 20%. The temperature drop was smaller with a higher flow rate, with the temperature dropping from 350K to 338K in an uninsulated pipe of 100 m length. The temperature drop for lower flow rate could be considered significant to reach wax appearance temperature during dynamic cooling. This study would be a supporting step to involve simulation in predicting the properties of waxy crude oil in different pipe sizes

PREDICTION OF PERFORMANCE AND EMISSION OF COMPRESSED NATURAL GAS (CNG) IN A SUPERCHARGED DIRECT INJECTION SPARK IGNITION ENGINE USING ARTIFICIAL NEURAL NETWORK

Emissions of greenhouse gases such as carbon dioxide, nitrogen oxide and some hydrocarbons have been the main causes of global warming and have posed severe impacts on climate changes. Consequently, the focus on sustainable developments has swiftly grown in recent years. The utilisation of Compressed Natural Gas (CNG) in the spark ignition (SI) engine or using as dual fuel in compression ignition (CI) engine is getting remarkable attention nowadays. In this paper, performance and emission of CNG in supercharged direct injection spark ignition engine were predicted via Artificial Neural Network (ANN). The Levenberg Marquardt training algorithm was used due to its fast response, easy operation and high accuracy. The models’ results are compared with experimental results available from a previous study by the author. It was observed that R2 values for both performance and emission of CNG were higher than 98%, indicating good prediction. Following the training, high accuracy with value higher than 95% was observed for both analyses. Hence, it can be concluded that ANN is a promising technique for the prediction of performance and emission of CNG in direct injection spark ignition engine that consists of numerous inputs and output conditions

Heterostructured composite of NiFe-LDH nanosheets with Ti4O7 for oxygen evolution reaction

Developing oxygen evolution reaction (OER) electrocatalyst based on earth-abundant materials holds great promise for ascertaining water-splitting to surmount its deprived kinetics. In this regard, NiFe-LDH (layered double hydroxide) receives considerable attention owing to their layered structure. However, they still suffer from poor electronic conductivity and structural stability. We combined NiFe-LDH nanosheets with Magnéli phase Ti4O7 into a heterostructured composite. A series of analyses reveal that decorating Ti4O7 facilitates charge transfer to enhance the conductivity of NiFe-LDH-Ti4O7. During electrochemical measurement, Ni2+ is transformed to metastable Ni3+ (Ni (OH)→ NiOOH) before the OER onset potential. Thus, the presence of Ni3+ as the main active sites could improve the chemisorption of OH− to facilitate OER. As a result, the NiFe-LDH-Ti4O7 catalyst delivers as low as onset potential (1.43 V). Combining the holey structure (NiFe-LDH and Ti4O7) and the defect engineering generated on NiFe-LDH-Ti4O7 as a synergistic effect improves the OER performance. The inclusion of Ti4O7 in the composite leads to more vacancy sites, as evidenced by the extended X-ray absorption fine structure (EXAFS) analysis. The obtained defective structure with a low coordination environment would improve the electronic conductivity and facilitate the adsorption process of H2O onto metal cations, thereby increasing the intrinsic catalytic activity of NiOOH. The strong coupling of NiFe-LDH and Ti4O7 also increases the stability, and the heterostructured composite helps maintain the structural robustness of the LDH

New Higgs Production Mechanism in Composite Higgs Models

Composite Higgs models are only now starting to be probed at the Large Hadron Collider by Higgs searches. We point out that new resonances, abundant in these models, can mediate new production mechanisms for the composite Higgs. The new channels involve the exchange of a massive color octet and single production of new fermion resonances with subsequent decays into the Higgs and a Standard Model quark. The sizable cross section and very distinctive kinematics allow for a very clean extraction of the signal over the background with high statistical significance. Heavy gluon masses up to 2.8 TeV can be probed with data collected during 2012 and up to 5 TeV after the energy upgrade to $\sqrt{s}=14$ TeV.Comment: 27 pages, 22 figures. V2: typos corrected, matches published versio

Prevalence and associated risk factors of malaria among adults in East Shewa Zone of Oromia Regional State, Ethiopia: a cross-sectional study

BACKGROUND: Malaria is one of the most important causes of morbidity and mortality in sub-Saharan Africa. The disease is prevalent in over 75% of the country's area making it the leading public health problems in the country. Information on the prevalence of malaria and its associated factors is vital to focus and improve malaria interventions. METHODS: A cross-sectional study was carried out from October to November 2012 in East Shewa zone of Oromia Regional State, Ethiopia. Adults aged 16 or more years with suspected malaria attending five health centers were eligible for the study. Logistic regression models were used to examine the effect of each independent variable on risk of subsequent diagnosis of malaria. RESULTS: Of 810 suspected adult malaria patients who participated in the study, 204 (25%) had microscopically confirmed malaria parasites. The dominant Plasmodium species were P. vivax (54%) and P. falciparum (45%), with mixed infection of both species in one patient. A positive microscopic result was significantly associated with being in the age group of 16 to 24 years [Adjusted Odds Ratio aOR 6.7; 95% CI: 2.3 to 19.5], 25 to 34 years [aOR 4.2; 95% CI: 1.4 to 12.4], and 35 to 44 years [aOR 3.7; 95% CI: 1.2-11.4] compared to 45 years or older; being treated at Meki health center [aOR 4.1; 95% CI: 2.4 to 7.1], being in Shashemene health center [aOR = 2.3; 95% CI: 1.5 to 4.5], and living in a rural area compared to an urban area [aOR 1.7; 95% CI: 1.1 to 2.6)]. CONCLUSION: Malaria is an important public health problem among adults in the study area with a predominance of P. vivax and P. falciparum infection. Thus, appropriate health interventions should be implemented to prevent and control the disease

Genome sequence data from 17 accessions of Ensete ventricosum, a staple food crop for millions in Ethiopia.

Published online: 11 Mar 2018We present raw sequence reads and genome assemblies derived from 17 accessions of the Ethiopian orphan crop plant enset (Ensete ventricosum (Welw.) Cheesman) using the Illumina HiSeq and MiSeq platforms. Also presented is a catalogue of single-nucleotide polymorphisms inferred from the sequence data at an average density of approximately one per kilobase of genomic DNA

МОДЕЛИРОВАНИЕ ЗАВИСИМОСТИ ОТРАЖАЮЩЕЙ СПОСОБНОСТИ МИКРОЗЕРКАЛ ОПТОВОЛОКОННЫХ КОМПОНЕНТОВ ОТ ИХ ГЕОМЕТРИЧЕСКИХ ПАРАМЕТРОВ

The main goal of investigation is defining dependency of geometrical parameters of MEMS-mirrors components in opto-electro-mechanical switches to losses appeared in such systems. Geometrical parameters of mirrors such as roughness and curvature, which had appeared on stage of development, have influence on losses in fiber-optic communication lines. The modeling dependency of reflection possibility of micro-mirrors to values of roughness and curvature has been conducted.Целью исследования является определение зависимости геометрических параметров компонентов МЭМС-зеркал в оптоэлектромеханических переключателях к оптическим потерям, возникающим в этих системах. Показано, что геометрические параметры: шероховатость и кривизна зеркал, возникающие на этапах их технологического производства, непосредственно влияют на потери в оптических линиях связи. Проведено моделирование зависимости отражательной способности микрозеркал от величин шероховатости и кривизны