12 research outputs found

    SME finance and the construction of value in Rwanda

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    Purpose This article explores how entrepreneurs, banks, the government and alternative lending respond to finance gaps for Small and Medium Enterprises (SME). This article considers valuation as a sociological construct where actors use different calculative devices, forming an assemblage that partly positions valuation of entrepreneurial finance as a contested and socially constructed process. Design/methodology/approach Drawing on the concept of ‘calculative devices’, the study articulates discursive institutional practices embedded within SME lending. This case study draws on analyses of 30 semi-structured interviews and archival data, government reports, and newspaper articles. Findings The study identified three triggers in Rwanda that were rooted in the informal and unincorporated nature of the SME governance structure; the lack of capacity for SME owners to manage their own projects; and normalising language around collateral requirements that marginalised the realities of SMEs; contributing to stagnation for SME finance. Practical implications The research provides direction for understanding how calculative devices create new forms of valuation of entrepreneurship in developing countries, particularly when human and non-human actors come together in an assemblage. The study calls for further research to demonstrate the embedded power of valuation practices and the performance of value in entrepreneurial finance. Originality/value The study brings new findings to the market creation literature by extending the notion of distributive calculative agency to SME finance. The study mobilises theory to interpret how discursive institutional practices are embedded within a country’s finance infrastructure, yielding unintended consequences for SME growth

    Морфологическая диагностика фолликулярных опухолей щитовидной железы

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    ЩИТОВИДНОЙ ЖЕЛЕЗЫ НОВООБРАЗОВАНИЯ /ДИАГНЭНДОКРИННЫХ ЖЕЛЕЗ НОВООБРАЗОВАНИЯЩИТОВИДНОЙ ЖЕЛЕЗЫ БОЛЕЗНИКАРЦИНОМА ПАПИЛЛЯРНАЯ ФОЛЛИКУЛЯРНАЯДИАГНОСТИКА ДИФФЕРЕНЦИАЛЬНА

    Simulation of Internet of Things Water Management for Efficient Rice Irrigation in Rwanda

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    The central role of water access for agriculture is a clear challenge anywhere in the world and particularly in areas with significant seasonal variation in rainfall such as in Eastern and Central Africa. The combination of modern sensor technologies, the Internet, and advanced irrigation equipment combined in an Internet of Things (IoT) approach allow a relatively precise control of agricultural irrigation and creating the opportunity for high efficiency of water use for agricultural demands. This IoT approach can thereby increase the resilience of agricultural systems in the face of complex demands for water use. Most previous works on agricultural IoT systems are in the context of countries with higher levels of economic development. However, in Rwanda, with a low level of economic development, the advantages of efficient water use from the application of IoT technology requires overcoming constraints such as lack of irrigation control for individual farmers, lack of access to equipment, and low reliability of power and Internet access. In this work, we describe an approach for adapting previous studies to the Rwandan context for rice (Oryza sativa) farming with irrigation. The proposed low cost system would automatically provide irrigation control according to seasonal and daily irrigational needs when the system sensors and communications are operating correctly. In cases of system component failure, the system switches to an alternative prediction mode and messages farmers with information about the faults and realistic irrigation options until the failure is corrected. We use simulations to demonstrate, for the Muvumba Rice Irrigation Project in Northeast Rwanda, how the system would respond to growth stage, effective rainfall, and evapotranspiration for both correct operation and failure scenarios

    Beacon-Enabled Cognitive Access for Dynamic Spectrum Access

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    In dynamic spectrum access networks, the unused licensed spectrum used by primary users (PU) is opened to unlicensed secondary users (SU) for improving spectrum efficiency. We design a simple time-based threshold policy for collective protection of PUs, enabled by an out-of-band channel. In particular, multiple SUs may be widely distributed in a geographic location. The interference that collocated SUs cause to each other, termed self-interference, becomes a major source that may degrade the SUs communication performance. We establish an analytical framework for carrier sense multiple access (CSMA) based coexistence mechanisms when integrated into a family of time based threshold policies, and study its performance though theoretical analysis

    An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

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    Tea is one of the most popular beverages in the world, and its processing involves a number of steps which includes fermentation. Tea fermentation is the most important step in determining the quality of tea. Currently, optimum fermentation of tea is detected by tasters using any of the following methods: monitoring change in color of tea as fermentation progresses and tasting and smelling the tea as fermentation progresses. These manual methods are not accurate. Consequently, they lead to a compromise in the quality of tea. This study proposes a deep learning model dubbed TeaNet based on Convolution Neural Networks (CNN). The input data to TeaNet are images from the tea Fermentation and Labelme datasets. We compared the performance of TeaNet with other standard machine learning techniques: Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). TeaNet was more superior in the classification tasks compared to the other machine learning techniques. However, we will confirm the stability of TeaNet in the classification tasks in our future studies when we deploy it in a tea factory in Kenya. The research also released a tea fermentation dataset that is available for use by the community

    A Data Descriptor for Black Tea Fermentation Dataset

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    Tea is currently the most popular beverage after water. Tea contributes to the livelihood of more than 10 million people globally. There are several categories of tea, but black tea is the most popular, accounting for about 78% of total tea consumption. Processing of black tea involves the following steps: plucking, withering, crushing, tearing and curling, fermentation, drying, sorting, and packaging. Fermentation is the most important step in determining the final quality of the processed tea. Fermentation is a time-bound process and it must take place under certain temperature and humidity conditions. During fermentation, tea color changes from green to coppery brown to signify the attainment of optimum fermentation levels. These parameters are currently manually monitored. At present, there is only one existing dataset on tea fermentation images. This study makes a tea fermentation dataset available, composed of tea fermentation conditions and tea fermentation images
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