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Analysis of Overdispersed Insect Count Data from an Avocado Plantation in Thika, Kenya
Eric Ali Ibrahim,
Daisy Salifu,
Samuel Musili Mwalili,
Thomas Dubois,
Henri Edouard Zefack Tonnang
Issue:
Volume 8, Issue 1, February 2022
Pages:
1-10
Received:
10 December 2021
Accepted:
4 January 2022
Published:
16 February 2022
Abstract: Avocado (Persea americana) farming in East Africa has expanded since recent, contributing significantly toward economic growth and livelihood for small-scale farmers. However, insects attacking avocado fruits reduce fruit quality and size, causing massive losses. Previous studies have identified key avocado insect pests, their temporal population patterns and how landscape vegetation productivity influences their population dynamics. This research analyzed insect count data collected on Bactrocera dorsalis and Ceratitis spp. in an avocado plantation in Thika, Kenya over a successive period of time, as part of pest management. These data are characterized by overdispersion due to aggregation behaviour of the insects in their habitat and serial correlations since the count data were collected over a successive period of time. Analyzing these data becomes complicated because of overdispersion and the serial correlation in the data. In this study, we explored variants of generalized linear models (GLMs) with a sinusoidal component over time; and with and without timescale decomposition of covariates (weather variables). All GLM variants were fitted assuming the negative binomial distribution to account for overdispersion. Based on the Akaike information criterion (AIC), GLMs with decomposed covariates had lower AIC values than GLMs without decomposed covariates for both B. dorsalis and Ceratitis spp., and therefore GLMs with a sinusoidal component and decomposed covariates under negative binomial distribution were the best choice for these data. The contribution of the preceding weekly insect pest counts in all models was statistically significant. The study established that both abiotic and biotic factors drive insect pest infestation.
Abstract: Avocado (Persea americana) farming in East Africa has expanded since recent, contributing significantly toward economic growth and livelihood for small-scale farmers. However, insects attacking avocado fruits reduce fruit quality and size, causing massive losses. Previous studies have identified key avocado insect pests, their temporal population p...
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Creation of a Unified Cloud Readiness Assessment Model to Improve Digital Transformation Strategy
Issue:
Volume 8, Issue 1, February 2022
Pages:
11-17
Received:
8 January 2022
Accepted:
6 February 2022
Published:
16 February 2022
Abstract: Digital transformation can disrupt any organization in any industry, but few organizations have successfully transformed. For an organization to transform digitally, a firm must adopt new technologies that enable it to change how it creates value. One of the most crucial new technologies organizations need to facilitate digital transformation is Cloud services. Cloud-based technologies are necessary for digital transformation because they allow a firm to cost-effectively obtain needed infrastructure capacity, processing, and developmental flexibility to support advanced analytical tools and methods. Implementing and adopting cloud services can be challenging and requires firm leaders and transformation teams to have an effective strategy that requires understanding a company's cloud readiness. Multiple organizational factors can impact cloud readiness, and depending on a firm's strengths or weaknesses, each element will support or hinder the adoption of cloud services. Understanding a firm's cloud readiness has been shown to improve an organization's adoption of cloud services, but assessing readiness can also be challenging since different assessment models determine readiness in varying ways. Readiness assessment models range from generic technology adoption models to specific cloud services readiness models, and a unified approach is needed that combines the strengths of each model to create a more comprehensive assessment model. A meta-analysis of current readiness assessment models was conducted to identify what crucial factors of an organization need to be assessed. The findings show that there seems to be significant agreement that a company's strategy, current technology, existing operations, and external factors are crucial readiness factors. More recent assessment models also identify gaps in past models, especially on human capital capabilities, system flexibility needs, and security. A more unified cloud assessment model is proposed based on the analysis showing that a firm's readiness should be based on seven crucial factors: strategy, technology, current operations, external requirements, human capital, system flexibility, and security. The new proposed assessment model provides a more comprehensive assessment of a firm's cloud readiness and enables organizations to create an improved adoptions strategy that will better support a company's digital transformation.
Abstract: Digital transformation can disrupt any organization in any industry, but few organizations have successfully transformed. For an organization to transform digitally, a firm must adopt new technologies that enable it to change how it creates value. One of the most crucial new technologies organizations need to facilitate digital transformation is Cl...
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Using Human Intelligence to Test the Impact of Popular Preprocessing Steps and Feature Extraction in the Analysis of Human Language
W. Randolph Ford,
Ingrid G. Farreras
Issue:
Volume 8, Issue 1, February 2022
Pages:
18-22
Received:
10 January 2022
Accepted:
5 February 2022
Published:
16 February 2022
Abstract: More than half a century has passed since Chomsky’s theory of language acquisition, Green and colleagues’ first natural language processor Baseball, and the Brown Corpus creation. Throughout the early decades, many believed that once computers became powerful enough, the development of A.I. systems that could understand and interact with humans using our natural languages would quickly follow. Since then, Moore’s Law has basically held; computer storage and performance has kept pace with our imaginations. And yet, 60 years later, even with these dramatic advances in computer technology, we still face major challenges in using computers to understand human language. The authors suggest that these same exponential increases in computational power have led current efforts to rely too much on techniques designed to exploit raw computational power and, in so doing, efforts have been diverted from advancing and applying the theoretical study of language to the task. In support of this view, the authors provide empirical evidence exposing the limitations of techniques – such as n-gram extraction – used to pre-process language. In addition, the authors conducted an analysis comparing three leading natural-language processing question-answering systems to human performance, and found that human subjects far outperformed all question answering-systems tested. The authors conclude by advocating for efforts to discover new approaches that use computational power to support linguistic and cognitive approaches to natural language understanding, as opposed to current techniques founded on patterns of word frequency.
Abstract: More than half a century has passed since Chomsky’s theory of language acquisition, Green and colleagues’ first natural language processor Baseball, and the Brown Corpus creation. Throughout the early decades, many believed that once computers became powerful enough, the development of A.I. systems that could understand and interact with humans usi...
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