COMPOSITE FLOUR DEVELOPMENT FOR INJERA

ABSTRACT: A composite flour has been developed to simulate an expensive cereal grain, tef (Eragrostis tef) for making injera, by incorporating cheaper grains. Sixty four combinations were baked, their physical characteristics and shelf-life tested. The results were statistically analyzed using mean scores of texture, elasticity , and reconstitution properties. The triangle and duo-trio tests were used for panel selection; paired comparison preference and declared control difference tests were employed for sensory evaluation, and the best composite flour to imitate tef was a combination of [tef 35% wheat (Triticum durum) 25% and sorghum (Sorghum vulgare) kafir group 40%]. The nutritive value of the new product correspond with that of tef and a 27% cost reduction was accomplished for each injera. [Ethiop. J. Health Dev. 1993;7(2):71-77

From seed security to food security: validating ‘Triple S’ seed conservation technology in new contexts.

This flyer outlines the progress made between 2014 and 2015 regarding the validation of Triple S (Storage in Sand and Sprouting) technology in the Southern Nations Nationalities and Peoples’ Region (SNNPR) Ethiopia

The Effect of Relationship Marketing on Customers’ Loyalty (Evidence from Zemen Bank)

AbstractEvery firm without good marketing team and strategies is bound to fail. In order to be competitive and profitable in the industry, bankers should acquire and retain profitable customers. Definitely this is done with Relationship marketing. Relationship marketing is a philosophy of doing business, a strategic orientation that focuses on keeping and improving current customers rather than acquiring new customers. The aim of this study is to investigate the influence of Relationship marketing underpinnings on customers‟ loyalty. The study also investigated the mediating role of top management commitment between relationship marketing and customers‟ loyalty. The study is based on information collected from both primary and secondary sources of data. The sample for this study is taken from customers of Zemen bank in Addis Ababa. Data analyses were done using frequencies, percentages, means, standard deviations, cross-tabulations, and tables followed by discussions. Moreover, inferential statistics of bivariate correlations and simple and multiple regressions were used. Findings of this study reveal that there is a significant and strong correlation between relationship marketing and customers‟ loyalty. Moreover, the study reveals that there is significant effect of trust, commitment, communication, and gratitude on predicting customers’ loyalty. Of these relationships, Gratitude emerged as the strongest factor which influences customers’ loyalty while conflict management remained statistically insignificant and negligible influence on customers’ loyalty in the cumulative model. However, all underpinnings had statistically significant influence on customers’ loyalty as individual. Moreover, management commitment had mediating effect between relationship marketing and customers’ loyalty. Depending on the findings, practical implications of this study is in order to ensure loyalty among bank customers; bankers should build a better relationship marketing strategy. Therefore, banks competitiveness and profitability will depend on their ability to build strong relationship bonds with their customers continuously. Furthermore, future research directions were also suggested on this study

Recommending Issue Reports to Developers Using Machine Learning

Tarkvarasüsteemide arendust viiakse tihti läbi iteratiivse protsessina ning erinevad tööüleasnded tekkivad siis kui leitakse defekte või tekib vajadus uue funktsionaalsuse järele. Need ülesanded salvestatakse probleemihalduse süsteemi, kust arendajad saavad sisendit oma tööle. Ülesannete jaotamine arendajatele võib toimude mitmel eri viisil. Üks populaarsemaid lähenemisi näeb ette, et arendajad valivad ise ülesandeid, mis neid huvitavad. Suurtes projektides võib see aga muutuda keeruliseks: ülesannete suure arvu tõttu on arendajatel raske aegsasti valida omale huvitav tööülesanne. Selle probleemi leevendamiseks esitatakse antud töös masinõppel põhinev soovitussüsteem, mis on võimeline probleemihalduse süsteemi ajaloost õppima milliseid ülesandeid on iga arendaja eelnevalt täitnud ja selle põhjal soovitada neile uusi ülesandeid. Süsteemi arendamiseks koguti 6 erinevast avatud lähtekoodiga projektist ülesandeid, kasutati erinevaid masinõppe meetodeid ja võrreldi tulemusi, et leida sobivaim. Soovitussüsteemi jõudluse hindamiseks kasutati täpsuse (precision), saagise (recall), f1-skoori (f1-score) ja keskmise täpsuse (mean average precision) mõõdikuid. Tulemused näitavad, et 100 tööülesande kirjelduse põhjal 10 igale arendajale sobivaima soovitamise puhul võib saavutada saagise 52.9% ja 96% vahel, mis on 6 kuni 9.5 korda parem 10 juhusliku töökirjelduse valimisest. Sarnased parandused saavutati ka teistes mõõdikutes.The development of a software system is often done through an iterative process and different change requests arise when bugs and defects are detected or new features need to be added. These requirements are recorded as issue reports and put in the backlog of the software project for developers to work on. The assignment of these issue reports to developers is done in different ways. One common approach is self-assignment, where the developers themselves pick the issue reports they are interested in and assign themselves. Practising self-assignment in large projects can be challenging for developers because the backlog of large projects become loaded with many issue reports, which makes it hard for developers to filter out the issue reports in line with their interest. To tackle this problem, a machine learning-based recommender system is proposed in this thesis. This recommender system can learn from the history of the issue reports that each developer worked on previously and recommend new issue reports suited to each developer. To implement this recommender system, issue reports were collected from 6 different opensource projects and different machine learning techniques were applied and compared in order to determine the most suitable one. For evaluating the performance of the recommender system, the Precision, Recall, F1-score and Mean Average Precision metrics were used. The results show that, from a backlog of 100 issue reports, by recommending the top 10 issue reports to each developer a recall ranging from 52.9% up to 96% can be achieved, which is 6 up to 9.5 times better than picking 10 issue reports randomly. Comparable improvements were also achieved in the other metrics

Factors affecting Attitudes towards Adoption of Mobile Banking: Users and Non-Users Perspectives

The advent and dispersal of technology is an interesting area of study since its success is dependent on the attitude for the adoption of it by customers. Extant literature indicates that mobile banking is the least adopted type of electronic banking when compared to other types of banking like Automated Teller Machine (ATM), despite its being the cheapest and quickest mode of communication. This study empirically examines and tests factors affecting users and non-users’ attitude towards the adoption of mobile banking. Data were collected from 256 participants both from users and non-users of mobile banking. Collected data were analyzed using chi-square, ANOVA, and correlation analysis. Findings indicate that trust, perceived ease of use, relative advantage, and compatibility have strong correlations with both users and non-users’ adoption towards mobile banking. However, perceived risk is found to have no significant relationship with users and non-users’ attitude towards the adoption of mobile banking.  Moreover, it is found that there is a difference between users and non-users’ attitude towards the adoption of mobile banking.  Furthermore, managerial implications, limitations of the study and future research directions were discussed

Energy Transition Under the New NAFTA: Challenges in the Critical Minerals Supply Chain

Demand for critical minerals, battery metals, and the nearshoring of electric vehicle (EV) manufacturing have implications for all trading partners of the updated North American Free Trade Agreement, now called the United States-Mexico-Canada Agreement (USMCA). North American EV manufacturing is driven by initiatives such as the battery belt in the US and Canada’s commitment to clean technology. Mexico, as a major auto-component manufacturer and producer of critical minerals, holds a significant role in supporting the regional supply chain. However, recent developments in Mexican natural resource policy, including the nationalization of lithium deposits and exploration moratoriums, present challenges for foreign miners operating in Mexico, including the risk of future limited participation in the mining sector. Canadian miners hold a dominant role in Mexican mineral exploration, and Mexico is Canada’s third-largest trading partner. The political landscape in Mexico, with the ruling Morena party controlling both the national government and majority of state governments, further complicates the situation. Policy changes in 2023 to the mining sector’s regulatory requirements are the most significant reforms to the sector since the early 1990s. The reforms are in response to prominent, long-standing grievances from various non-industry stakeholders and seek to mitigate against future negative social and environmental impacts of mining. The reforms include shorter mining concession permits, stricter environmental impact assessments, and new permitting procedures on water use

Determinants of iodine deficiency in school children in different regions of Ethiopia

Objective: To determine the magnitude of goitre in school children and measure indicators of iodine deficiency including the most commonly consumed staple foods.Design: A cross-sectional study.Setting: Ten villages from four administrative regions of Ethiopia.Subjects: A total of 2485 randomly selected elementary school children were examined for clinical signs of goitre. Indicators of iodine deficiency disorders (IDD) assessed were urinary iodine excretion (UIE) rate, iodine concentration in water, the commonly consumed individual foods of plant origin and milk, and bacterial contamination of drinking water.Results: The gross prevalence (mean of male and female values) among school children was 53.3%. The prevalence was higher in females (56.1%) than in males (50.8%). The highest prevalence (82 and 91%, respectively) were observed in the villages of Lotte and Kodowono and the lowest (31%) in the village of Abossara. Of the urinary measurements, 70% of the samples showed moderate and 30% mild iodine deficiency. Levels of iodine in water and individual food samples were low in general. Breast milk iodine content was similarly low and related to the maternal daily iodine intake which may affect the nutritional status of the nursing infant. The study also provides further evidence that coliforms and E. coli isolated from drinking water contribute to the high incidence of endemic goitre other than iodinedeficiency.Conclusion: It is difficult to obtain a sufficient iodine intake in the survey villages as the individual foods are very low in the element. However, IDD can be prevented by ensuring normal iodine nutrition through instituting ways that avail iodinated salt to the surveypopulation

Establishing ranges of clinical normal limits and comparison with adopted limits for adult population

Abstract: It is important to know the normal limits for each test in each laboratory. In most cases the normal limits established by others have been adopted and used as reference values. In view of this an attempt is made in this paper to establish ranges of clinical normal limits for adults. Eight determinations SGOT, SGPT, ALP, BILD, BILT, FBS, UREA, and CREATININE were included in the study. Normal limits were established based on a validated statistical method. Comparision was made with adopted normal limits in use in laboratories. For most tests notable differences in limits, particularly from the side of abnormal values, have been observed which resulted in high misclassification of laboratory test values. [Ethiop. J. Health Dev. 1997;11(2):97-101

Properties of Generalised Lattice Ordered Groups

A partially ordered group (po-group) is said to be a generalised lattice ordered group (gl-group) if the underlying poset is a generalised lattice. This paper is a study of some properties of finite subsets of a generalised lattice ordered group (gl-group). Finally obtained a lattice ordered group (l-group) from the given interally closed gl-group and concluded that every integrally closed gl-group is distributive