DETERMINANTS OF ADAPTATION TO DEFORESTATION AMONG FARMERS IN MADAGALI LOCAL GOVERNMENT AREA OF ADAMAWA STATE, NIGERIA

The study examined the determinants of adaptation to deforestation among farmers in Madagali Local Government Area of Adamawa state, Nigeria. Structured interview schedule were used to obtain information from 200 respondents selected through simple random sampling techniques. The data collected were analyzed using descriptive (frequencies and percentages) and inferential (chi-square test) statistics. The result indicated that majority (84%) of the respondents were male with 21-40 years of age representing 58.50%. The study also showed that most (45%) of the respondents had attained post primary education with majority (80%) having 6 and above years of farming experience. The respondents perceived fuel wood extraction as the major (42%) cause of deforestation in the study area. The result showed that the most (30.50%) frequently employed adaptation strategy against deforestation was reducing quantity of fire wood consumption. The study further showed that the factors which significantly influenced adaptation to deforestation among the respondents were age, farming experience and educational status with X2=9.216, 8.697 and 11.238 at P<0.05 respectively. While those factors which did not influence adaptation to deforestation among the respondents were gender, access to agricultural credit and access to extension services with X2=1.286, 7.923 and 5.862 at P<0.05 respectively. The major constraints faced by respondents in adaptation to deforestation were lack of capital and lack of accessible alternative energy. The study recommends that awareness campaign should be mounted to increase the level of knowledge of respondents on the significance of adaptation to deforestation. Respondents should also be encouraged towards establishment of adaptation cooperative societies in order to take advantage of some government policies and programmes

Seed Health, Quality Test, and Control of Seed-borne Fungi of Some Improved and Local Cultivars of Rice (Oryza sativa L.) in Kano, Northwestern Nigeria

This research was carried out to evaluate the health and quality of rice seed. The germination of seed and presence of rice seed related fungi were recorded and used to evaluate the effect of seed dressing chemicals on germination and vigor index over untreated rice seeds. Seven cultivars commonly grown in Kano, Kano State, Northwestern Nigera “FARO 52” (WITA), “FARO 44” (SIPI), “FARO 60” , (improved varieties), “Kwandala” , “Jamila” , Ex-china, and “JIF” (local varieties) were used in this study. The seed dressing chemicals used were Apron Star 42 WS, Dress Force 42WS and ZEB-Care 80%WP. This study was performed under three main tests, i.e dry inspection, blotter tests, agar plate and microscopic examination. The highest number of healthy seeds (94.16%) was recorded from “JIF” variety and lowest (64.77%) from “Jamila” . The highest number of deformed seeds was observed from variety “FARO 44” whereas the lowest noted on “JIF” . The identified fungi were Fusarium spp., Bipolaris oryzae, Aspergillus flavus, Curvularia lunata, Aspergillus niger, and Nigrospora oryzae., Rhizoctonia spp. and Rhizopus spp. Highest seed infection was recorded for A. flavus, A. niger, and Fusarium spp., and the least with C. lunata and N. oryzae. Treated seeds with Zeb-care (Mancozeb 80% WP), increased their vigor index over untreated by 62.78% and can be recommended as seed dressing chemical for optimun control of rice seed- borne pathogens

K-means clustering to improve the accuracy of decision tree response classification.

The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification task, any irrelevant or unreliable tagging of response classes represented will result in low accuracy. This study focused on improving dialogue act classification of a user utterance into a response class by clustering the semantic and pragmatic features extracted from each user utterance. A Decision tree approach is used to classify 64 mixed-initiative, transaction dialogue corpus in theater domain. The experiment shows that by using clustering technique in pre-processing stage for re-tagging response classes, the Decision tree is able to achieve 97.5% recognition accuracy in classification, better than the 81.95% recognition accuracy when using Decision tree alone

Improving accuracy of intention-based response classification using decision tree.

This study focused on improving the dialogue act classification to classify a user utterance into a response class using a decision tree approach. Decision tree classifier is tested on 64 mixed-initiative, transaction dialogue corpus in theater domain. The result from the comparative experiment show that decision tree able to achieve 81.95% recognition accuracy in classification better than the 73.9% obtained using Bayesian networks and 71.3% achieved by using Maximum likelihood estimation. This result showed that the performance of decision tree as classifier is well suited for these tasks

A weighted hard combination scheme for cooperative spectrum sensing in cognitive radio sensor networks

Multi-user spatial sensing diversity exploration through cooperation spectrum sensing greatly improves sensing performance. However, high communication overhead and energy costs for exchanging sensing results may limit its viability in a realistic large scale resource constraint network such as cognitive radio wireless sensor networks. This paper presents a Weighted Hard Combination (WHC) scheme that combines features of both quantized and hard combining schemes to minimize energy cost for reporting sensing result and improve primary user detection performance in cooperative sensing. We evaluate the effectiveness of the scheme through simulation. Performance comparison of the WHC scheme in terms of detection performance, reporting energy cost and reporting time ratio with conventional hard combination, soft combination and quantized schemes indicates viability of the scheme. The results indicate that the WHC scheme minimizes reporting energy cost by 70% and improves detection performance by 5.6% compared to the quantized 3-bits scheme

Energy-aware cluster based cooperative spectrum sensing for cognitive radio sensor networks

Energy efficient spectrum sensing and data communication to extend the lifetime of cognitive radio sensor network is becoming increasingly important due to resource constraint of CR-WSN inherent from WSN. This paper presents an energy-aware clustering (EAC) algorithm that enhances spectrum sensing performance and reduces network energy consumption thereby prolonging lifetime of the network. We derived network wide energy consumption model in terms of spectrum sensing energy consumption, intra cluster and inter clusters energy consumptions, and then determined the optimal number of clusters for the network. Through numerical analysis, we evaluate the effectiveness of the proposed algorithm in terms of minimizing network wide energy consumption and improving spectrum sensing performance

An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach

Synthesis, physicochemical, conformation and quantum calculation of novel N-(1-(4-bromothiophen-2-yl)ethylidene)-2-(piperazin-1-yl)ethanamine Schiff base

N-(1-(4-bromothiophen-2-yl)ethylidene)-2-(piperazin-1-yl)ethanamine Schiff base ligand was prepared in very good yield by condensation of equimolar amounts of 1-(4-bromothiophen-2-yl)ethanone with 2-(piperazin-1-yl)ethanamine under reflux condition using alcohol media. The desired Schiff base was analyzed on the basis of its MS, elemental analysis, UV-visible, FT-IR and NMR analysis. The E and Z optimization was performed to figure out the most stable isomer. Several DFT quantum calculation like: TD-SCF, MPE, IR-vibration, NMR, Mulliken population were carried out by B3LYP level of theory. The experimental analyses of the compound were compared to their theoretical coordinates