Unravelling technology-acceptance factors influencing farmer use of banana tissue culture planting materials in Central Uganda

Effective management of plant health is fundamental for food and income security to meet the growing demands of local and global markets. This however requires farmers’ adequate access to quality planting materials under the prevailing contextual and psycho-social factors. This study, anchored in the Unified Theory of Acceptance and Use of Technologies, unravels technology-acceptance factors that influence farmers’ intentions to use banana tissue culture planting materials in the control of Banana Xanthomonas Wilt. Data were collected from 248 randomly sampled banana farmers using a structured questionnaire and analyzed using structural equation modelling to examine hypothesized paths in the uptake of banana tissue culture planting materials. Results show that farmer intentions to use tissue culture planting materials are dependent on two constructs: social influence and farmer innovativeness. However, social influence is the main predictor of intentions to use tissue culture planting materials. In particular, farmer innovativeness mediates facilitating conditions and social influence in predicting intentions to use tissue culture planting materials. Thus, this study reveals two factors that influence farmer intentions to use tissue culture planting materials: social influence and farmer innovativeness. The findings imply that social influence and farmer innovativeness are critical in disseminating novel agricultural technologies in Uganda and elsewhere

On the performance, availability and energy consumption modelling of clustered IoT systems

Wireless sensor networks (WSNs) form a large part of the ecosystem of the Internet of Things (IoT), hence they have numerous application domains with varying performance and availability requirements. Limited resources that include processing capability, queue capacity, and available energy in addition to frequent node and link failures degrade the performance and availability of these networks. In an attempt to efficiently utilise the limited resources and to maintain the reliable network with efficient data transmission; it is common to select a clustering approach, where a cluster head is selected among the diverse IoT devices. This study presents the stochastic performance as well as the energy evaluation model for WSNs that have both node and link failures. The model developed considers an integrated performance and availability approach. Various duty cycling schemes within the medium-access control of the WSNs are also considered to incorporate the impact of sleeping/idle states that are presented using analytical modeling. The results presented using the proposed analytical models show the effects of factors such as failures, various queue capacities and system scalability. The analytical results presented are in very good agreement with simulation results and also present an important fact that the proposed models are very useful for identification of thresholds between WSN system characteristics

Packet arrival analysis in wireless sensor networks

Distributed sensor networks have been discussed for more than 30 years, but the vision of Wireless Sensor Networks (WSNs) has been brought into reality only by the rapid advancements in the areas of sensor design, information technologies, and wireless networks that have paved the way for the proliferation of WSNs. The unique characteristics of sensor networks introduce new challenges, amongst which prolonging the sensor lifetime is the most important. WSNs have seen a tremendous growth in various application areas including health care, environmental monitoring, security, and military purposes despite prominent performance and availability challenges. Clustering plays an important role in enhancement of the life span and scalability of the network, in such applications. Although researchers continue to address these grand challenges, the type of distributions for arrivals at the cluster head and intermediary routing nodes is still an interesting area of investigation. Modelling the behaviour of the networks becomes essential for estimating the performance metrics and further lead to decisions for improving the network performance, hence highlighting the importance of identifying the type of inter-arrival distributions at the cluster head. In this paper, we present extensive discussions on the assumptions of exponential distributions in WSNs, and present numerical results based on Q-Q plots for estimating the arrival distributions. The work is further extended to understand the impact of end-to-end delay and its effect on inter-arrival time distributions, based on the type of medium access control used in WSNs. Future work is also presented on the grounds that such comparisons based on simple eye checks are insufficient. Since in many cases such plots may lead to incorrect conclusions, demanding the necessity for validating the types of distributions. Statistical analysis is necessary to estimate and validate the empirical distributions of the arrivals in WSNs

Does the assumption of exponential arrival distributions in wireless sensor networks hold?

Wireless Sensor Networks have seen a tremendous growth in various application areas despite prominent performance and availability challenges. One of the common configurations to prolong the lifetime and deal with the path loss phenomena having a multi-hop set-up with clusters and cluster heads to relay the information. Although researchers continue to address these challenges, the type of distribution for arrivals at the cluster head and intermediary routing nodes is still an interesting area of investigation. The general practice in published works is to compare an empirical exponential arrival distribution of wireless sensor networks with a theoretical exponential distribution in a Q-Q plot diagram. In this paper, we show that such comparisons based on simple eye checks are not sufficient since, in many cases, incorrect conclusions may be drawn from such plots. After estimating the Maximum Likelihood parameters of empirical distributions, we generate theoretical distributions based on the estimated parameters. By conducting Kolmogorov-Smirnov Test Statistics for each generated inter-arrival time distributions, we find out, if it is possible to represent the traffic into the cluster head by using theoretical distribution. Empirical exponential arrival distribution assumption of wireless sensor networks holds only for a few cases. There are both theoretically known such as Gamma, Log-normal and Mixed Log-Normal of arrival distributions and theoretically unknown such as non-Exponential and Mixed cases of arrival in wireless sensor networks. The work is further extended to understand the effect of delay on inter-arrival time distributions based on the type of medium access control used in wireless sensor networks

User guide to the toolbox for working with root, tuber and banana seed systems. RTB User guide

This user guide to the Toolbox for working with root, tuber and banana seed systems introduces tools to diagnose, evaluate, and improve seed systems of banana, cassava, potato, sweetpotato, and yam. As a whole, these crops are called roots, tubers and bananas, and they are crucial for food security and income generation, especially in developing countries. All of these crops are reproduced vegetatively, from roots, tubers, stems, suckers or vines. This bulky planting material is expensive to transport. Vegetative seed is perishable and (except for potatoes, yams and a few other exceptions) must be planted as fresh as possible, and it is more likely to carry pests and diseases than true seed. Besides these unique challenges, improved seed systems of root, tuber and banana crops give farmers the opportunity to boost their livelihoods by accessing better quality planting material from landraces or improved varieties that are high yielding, resistant to stresses, more nutritious or more responsive to consumer demand

Retrospective Analysis of Presentation, Treatment, and Outcomes of Multiple Myeloma at a Large Public Referral Hospital in Eldoret, Kenya

Purpose: Treatment patterns and survival outcomes of patients with multiple myeloma (MM) in Kenya have not been adequately characterized. The objectives of this study were to describe the clinical, laboratory, and imaging findings at diagnosis, to describe the treatment offered, and to determine the survival outcomes of patients with MM over an 11-year period. Patients and methods: A retrospective chart review was carried out for all patients who were diagnosed and treated for MM at Moi Teaching and Referral Hospital from 2009 to 2019. The Kaplan-Meier method was used to estimate survival. Factors affecting survival were identified using univariate and multivariate analyses. Results: A total of 221 patient charts were analyzed of which 124 belonged to male patients (56.1%). The median age at diagnosis was 61 years. Bone pain was the most common presenting complaint observed in 69.6% of 194 patients assessed. Out of 102 patients who received imaging studies, 60 (58.8%) had lytic lesions, 30 (29.4%) had fractures, whereas 30 (29.4%) had spinal cord compression. Anemia, renal failure, and hypercalcemia were observed in 87/187 (46.5%), 22/161 (13.7%), and 23/42 (54.8%) patients, respectively. Thalidomide and dexamethasone (65.2%); bortezomib, thalidomide, and dexamethasone (14.6%); and melphalan and prednisolone (11.9%) were the most prescribed initial chemotherapy regimens among 219 patients analyzed. Overall survival at 1 and 5 years was 70% and 21%, respectively; median overall survival was 29.0 months. In multivariate analysis, male sex (hazard ratio [HR] 1.9), baseline anemia (HR 1.8), and baseline renal failure (HR 3.2) were associated with significantly shorter survival. Conclusion: Survival outcomes were poor despite increased use of multiagent-based chemotherapy regimens. Greater access to available diagnostics and treatments is required to achieve rational treatment and increased survival

Rapid and high throughput molecular identification of diverse mosquito species by igh resolution melting analysis

Mosquitoes are a diverse group of invertebrates, with members that are among the most important vectors of diseases. The correct identification of mosquitoes is paramount to the control of the diseases that they transmit. However, morphological techniques depend on the quality of the specimen and often unavailable taxonomic expertise, which may still not be able to distinguish mosquitoes among species complexes (sibling and cryptic species). High resolution melting (HRM) analyses, a closed-tube, post-polymerase chain reaction (PCR) method used to identify variations in nucleic acid sequences, has been used to differentiate species within the Anopheles gambiae and Culex pipiens complexes. We validated the use of PCR-HRM analyses to differentiate species within Anopheles and within each of six genera of culicine mosquitoes, comparing primers targeting cytochrome b (cyt b), NADH dehydrogenase subunit 1 (ND1), intergenic spacer region (IGS) and cytochrome c oxidase subunit 1 (COI) gene regions. HRM analyses of amplicons from all the six primer pairs successfully differentiated two or more mosquito species within one or more genera (Aedes (Ae. vittatus from Ae. metallicus), Culex (Cx. tenagius from Cx. antennatus, Cx. neavei from Cx. duttoni, cryptic Cx. pipiens species), Anopheles (An. gambiae s.s. from An. arabiensis) and Mansonia (Ma. africana from Ma. uniformis)) based on their HRM profiles. However, PCR-HRM could not distinguish between species within Aedeomyia (Ad. africana and Ad. furfurea), Mimomyia (Mi. hispida and Mi. splendens) and Coquillettidia (Cq. aurites, Cq. chrysosoma, Cq. fuscopennata, Cq. metallica, Cq. microannulatus, Cq. pseudoconopas and Cq. versicolor) genera using any of the primers. The IGS and COI barcode region primers gave the best and most definitive separation of mosquito species among anopheline and culicine mosquito genera, respectively, while the other markers may serve to confirm identifications of closely related sub-species. This approach can be employed for rapid identification of mosquitoes

Linking soil microbial community structure to potential carbon mineralization: A continental scale assessment of reduced tillage

Potential carbon mineralization (Cmin) is a commonly used indicator of soil health, with greater Cmin values interpreted as healthier soil. While Cmin values are typically greater in agricultural soils managed with minimal physical disturbance, the mechanisms driving the increases remain poorly understood. This study assessed bacterial and archaeal community structure and potential microbial drivers of Cmin in soils maintained under various degrees of physical disturbance. Potential carbon mineralization, 16S rRNA sequences, and soil characterization data were collected as part of the North American Project to Evaluate Soil Health Measurements (NAPESHM). Results showed that type of cropping system, intensity of physical disturbance, and soil pH influenced microbial sensitivity to physical disturbance. Furthermore, 28% of amplicon sequence variants (ASVs), which were important in modeling Cmin, were enriched under soils managed with minimal physical disturbance. Sequences identified as enriched under minimal disturbance and important for modeling Cmin, were linked to organisms which could produce extracellular polymeric substances and contained metabolic strategies suited for tolerating environmental stressors. Understanding how physical disturbance shapes microbial communities across climates and inherent soil properties and drives changes in Cmin provides the context necessary to evaluate management impacts on standardized measures of soil microbial activity