The research tends to evaluate the effectiveness of implementation modalities of Anchor Barrower Program ABP from the perspectives of benefiting smallholder farmers in northwestern Nigeria. Qualitative research paradigm will be deployed through focus group discussions with the benefiting smallholder farmers in seven northwestern states of Nigeria. Members of Rice Farmers Association of Nigeria RIFAN in each of the states served as a focus group. The Anchor Barrower Program implementation modalities issued by Central Bank of Nigeria CBN were used as thematic areas for the discussion. The research made interesting findings which highlights the scope for improvement in Anchor Barrower Program implementation modalities. The key of these areas include training aspect of the implementation modalities which in practical terms is significantly lacking. The strategic monitoring especially from the perspectives of farmers is also lacking. The activities and operations of vendors that supply inputs to farmers need to checked and strategically sanitized. The concerned of the farmers during town hall meetings need to attended to. The paper also highlights some important areas in which future research will be conducted. Abdul'azeez Ibrahim Badaru | Bello Lawal | Surayya Dahiru Alhassan "Effectiveness of the Implementation Modalities of Anchor Barrower Program – An Evaluation from the Perspectives of Benefiting Smallholder Farmers in Northwestern Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4 , June 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50082.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/50082/effectiveness-of-the-implementation-modalities-of-anchor-barrower-program-–-an-evaluation-from-the-perspectives-of-benefiting-smallholder-farmers-in-northwestern-nigeria/abdulazeez-ibrahim-badaru

Localization is one of the most important technologies for many applications in wireless sensor networks (WSNs). Node localization is the process of discovering the exact location of the node. If the number of nodes and network size increase, it becomes very arduous to localize the nodes whose result leads to complexity and path loss. In this paper, we proposed an approach called probabilistic based optimal node localization to obtain the location of node in the WSNs. This approach provides an enhanced channel pathloss model by capturing the features of the additive noise in WSN. In addition, the complexity has been minimized by discovering a lower bound of the non-convex function. The problem of non-convex optimization and subsequent nonlinear is solved with the help of relaxation to achieve a sub-optimal solution. Simulation results show that our proposed localization approach has got better performance for considered scenario settings.

Mobile IPv6 (MIPv6) and Hierarchical Mobile IPv6 (HMIPv6) both are the mobility management solutions proposed by the Internet Engineering Task Force (IETF) to support IP Mobility. It’s been an important issue, that upon certain condition, out of MIPv6 and HMIPv6 which one is better. In this paper an Optimum Efficient Mobility Management (OEMM) scheme is described on the basis of analytical model which shows that OEMM Scheme is better in terms of performance and applicability of MIPv6 and HMIPv6. It shows that which one is better alternative between MIPv6 and HMIPv6 and if HMIPv6 is adopted it chooses the best Mobility Anchor Point (MAP). Finally it is illustrated that OEMM scheme is better than that of MIPv6 and HMIPv6.

Past work suggests that anchor text is a good source of evidence that can be used to improve web searching. Two approaches for making use of this evidence include fusing search results from an anchor text representation and the original text representation based on a document's relevance score or rank position, and combining term frequency from both representations during the retrieval process. Although these approaches have each been tested and compared against baselines, different evaluations have used different baselines; no consistent work enables rigorous cross-comparison between these methods. The purpose of this work is threefold. First, we survey existing fusion methods of using anchor text in search. Second, we compare these methods with common testbeds and web search tasks, with the aim of identifying the most effective fusion method. Third, we try to correlate search performance with the characteristics of a test collection. Our experimental results show that the best performing method in each category can significantly improve search results over a common baseline. However, there is no single technique that consistently outperforms competing approaches across different collections and search tasks.

In this paper, we study and compare three different but related types of metadata about web documents: social annotations provided by readers of web documents, hyperlink anchor text provided by authors of web documents, and search queries of users trying to find web documents. We introduce a large research data set called CABS120k08 which we have created for this study from a variety of information sources such as AOL500k, the Open Directory Project, del.icio.us/Yahoo!, Google and the WWW in general. We use this data set to investigate several characteristics of said metadata including length, novelty, diversity, and similarity and discuss theoretical and practical implications.

The present study examines the performance of rectangular simply supported
einforced concrete (RC) beams with externally bonded reinforcement
EBR) made of carbon fibre-reinforced polymer (CFRP) plates. The
oad-carrying capacity of these CFRP EBR beams can be influenced
y the strengthening of their ends, which controls the premature
nd failure occurring when the anchorage length of the plates or
he shear span/depth ratio are low. Different basic techniques are
roposed to prevent this brittle failure: use of clamps at the ends
f the beam, bonding of lateral perpendicular or inclined strips
nd U-wrapping of shear spans with carbon fibre textile. The debonding
ode of failure has systematically taken the place of the concrete
over separation mode. Lateral bonding of CFRP strips and U-wrapping
sing carbon fibre textile are particularly efficient for the control
f debonding cracks and delay the premature end failure of the beams.
he loadcarrying capacity is enhanced, and the ductility is increased.

In this paper, we introduce hyperlink ensembles, a novel type of ensemble classifier for classifying hypertext documents. Instead of using the text on a page for deriving features that can be used for training a classifier, we suggest to use portions of texts from all pages that point to the target page. A hyperlink ensemble is formed by obtaining one prediction for each hyperlink that points to a page. These individual predictions for each hyperlink are subsequently combined to a final prediction for the class of the target page. We explore four different ways of combining the individual predictions and four different techniques for identifying relevant text portions. The utility of our approach is demonstrated on a set of Web-pages that relate to Computer Science Departments.

The structure of the web is increasingly being used to improve organization, search, and analysis of information on the web. For example, Google uses the text in citing documents (documents that link to the target document) for search. We analyze the relative utility of document text, and the text in citing documents near the citation, for classification and description. Results show that the text in citing documents, when available, often has greater discriminative and descriptive power than the text in the target document itself. The combination of evidence from a document and citing documents can improve on either information source alone. Moreover, by ranking words and phrases in the citing documents according to expected entropy loss, we are able to accurately name clusters of web pages, even with very few positive examples. Our results confirm, quantify, and extend previous research using web structure in these areas, introducing new methods for classification and description of pages.

In web classification, web pages from one or more web sites are assigned to pre-defined categories according to their content. Since web pages are more than just plain text documents, web classification methods have to consider using other context features of web pages, such as hyperlinks and HTML tags. In this paper, we propose the use of Support Vector Machine (SVM) classifiers to classify web pages using both their text and context feature sets. We have experimented our web classification method on the WebKB data set. Compared with earlier Foil-Pilfs method on the same data set, our method has been shown to perform very well. We have also shown that the use of context features especially hyperlinks can improve the classification performance significantly.

In this paper, we report on a set of experiments that explore the utility of making use of the structural information of WWW documents. Our working hypothesis is that it is often easier to classify a hypertext page using information provided on pages that point to it instead of using information that is provided on the page itself. We present experimental evidence that confirms this hypothesis on a set of Web-pages that relate to Computer Science Departments.

We propose a new algorithm called DK-vec for aligning pairs of Asian/Indo-European noisy parallel texts without sentence boundaries. DK-vec improves on previous alignment algorithms in that it handles better the non-linear nature of noisy corpora. The algorithm uses frequency, position and recency information as features for pattern matching. Dynamic Time Warping is used as the matching technique between word pairs. This algorithm produces a small bilingual lexicon which provides anchor points for alignment.