SURFACES REPRESENTATION WITH SHARP FEATURES USING SQRT(3) AND LOOP SUBDIVISION SCHEMES

ABSTRACT This paper presents a hybrid algorithm that combines features form bot

Analysis of Conduct and Performance of Dried Fish Market in Maiduguri Metropolis of Borno State, Nigeria

The study analyzed conduct and performance of dried fish markets in Maiduguri Metropolis of Borno State, Nigeria. Data were obtained using structured questionnaire. Three (3) major dried fish markets were purposely selected out of the seven (7) markets in the study area to reflect areas where dried fish is predominantly sold. A total of 100 respondents from the three (3) markets were randomly selected for the study. Descriptive statistics and budgetary techniques were used as analytical tools. The finding of the study reveals that (44.82%) and (33.33%) were obtained as market margin from sales of Grade C and Grade A dried fish respectively. The result of marketing cost and returns also reveals that capital invested constitutes (96.08%) of the total fixed cost, while transportation accounts for (30.55%) of the total variable cost. The result of market return (net returns) reveals that N25, 013,440.04 was obtained as net returns per week. The finding on marketing efficiency reveals (67.33%) was obtained per cartoons of dried fish sold. The result further indicates the efficiency ratio of 95.54 which is positive. The result of the market conduct reveals that about (40%) of the marketers get information on market situation through personal contact, (72%) price haggling, while (56%) said prices were set by both the sellers and the agents. It was recommended that local fish marketers should be organized into cooperative groups and government should adequately provide infrastructural facilities such as good roads and market facilities to dried fish marketers in the study area. Key words: Market Conduct, Market Performance, Dried Fish Market, Borno State,  Nigeri

Genotype diet interaction in Fayoumi and Rhode Island Red layers and their crosses

Fayoumi and Rhode Island Red (R.I.R.) layers and their two reciprocal crosses were distributed into 2 groups which received different diets in the laying period. The diets had the same calculated energy level and their total protein content differed by less than 1 p. 100, but one of them contained 40 p. 100 barley and the other contained none. With the barley-containing diet, feed consumption, egg mass, egg number and mean egg weight per hen were reduced, but the effects were more marked in the R.LR. line and one of the reciprocal crosses, with a significant genotype x diet interaction for egg mass, average clutch length, total feed intake and its residual component.Des poules Fayoumi et Rhode Island (R.LR.) et leurs 2 croisements réciproques ont été répartis en 2 groupes recevant un régime alimentaire différent en période de ponte. Les 2 régimes avaient la même teneur énergétique et un taux protéique différant de moins de 1 p. 100 mais l’un contenait 40 p. 100 d’orge, l’autre n’en contenait pas. En présence de la ration à base d’orge, la consommation alimentaire et la masse d’oeufs produite par poule, ainsi que le nombre et le poids moyen des oeufs, étaient abaissés, mais les effets étaient plus marqués dans la lignée R.LR. et dans l’un des croisements réciproques, avec une interaction régime x type génétique significative pour la masse d’oeufs, la longueur moyenne des séries de ponte, la consommation alimentaire totale et sa composante « résiduelle »

Genetic evaluation of some sesame genotypes for seed yield and its components

To study genetic variation, genetic parameters and selection criteria of seventeen sesame genotypes, a field experiment was conducted across different environments represented by two summer seasons of 2018 (E1) and 2019 (E2) at Etay-El-Baroud/Behaira Agricultural Research Station and one summer season of 2019 (E3) at Kafr-El-Hamam/Sharkia Agricultural Research Station, Agricultural Research Center, Egypt using a randomized complete block design with three replications for each environment. The promising sesame genotypes were  L25 for earliness in flowering at E1 and across environments, L101 for plant height and fruiting zone length when grown at E2 and L110 across environments, L35 for number of branches plant and seed yield per feddan when grown at E2 and across environments, L48 for capsules length when grown at E2  and L2 across environments, L82 for 1000-seed weight when grown at E3 and L35 across environments, L2 for seed weight per plant when grown at E1 and across environments and  L101 for seed oil content when grown at E1 and across environments. Among the most effective traits in improving seed weight per plant were fruiting zone length and number of branches per plant, as verified through correlation and path analyses at phenotypic and genotypic levels.These traits had the highest broad-sense heritability and genetic advance as percent of mean. Keywords: Correlation, Genetic variability, Heritability, Path analysi

Effect of 3 Key Factors on Average End to End Delay and Jitter in MANET

A mobile ad-hoc network (MANET) is a self-configuring infrastructure-less network of mobile devices connected by wireless links where each node or mobile device is independent to move in any desired direction and thus the links keep moving from one node to another. In such a network, the mobile nodes are equipped with CSMA/CA (carrier sense multiple access with collision avoidance) transceivers and communicate with each other via radio. In MANETs, routing is considered one of the most difficult and challenging tasks. Because of this, most studies on MANETs have focused on comparing protocols under varying network conditions. But to the best of our knowledge no one has studied the effect of other factors on network performance indicators like throughput, jitter and so on, revealing how much influence a particular factor or group of factors has on each network performance indicator. Thus, in this study the effects of three key factors, i.e. routing protocol, packet size and DSSS rate, were evaluated on key network performance metrics, i.e. average delay and average jitter, as these parameters are crucial for network performance and directly affect the buffering requirements for all video devices and downstream networks

3D scientific data mining in ion trajectories

In physics, structure of glass and ion trajectories are essentially based on statistical analysis of data acquired through experimental measurement and computer simulation. Invariably, the details of the structure-transport relationships in the data have been mistreated in favour of ensemble average. In this study, we demonstrate a visual approach of such relationship using surface-based visualisation schemes. In particular, we demonstrate a scientific datasets of simulated 3D time-varying model and examine the temporal correlation among ion trajectories. We propose a scheme that uses a three dimensional visual representation with colour scale for depicting the timeline events in ion trajectories and this scheme could be divided into two major part such as global and local time scale. With a collection of visual examples from this study, we demonstrate that this scheme may offer an effective tool for visually mining 3D timeline events of the ion trajectories. This work will potentially form a basis of a novel analysis tool for measuring the effectiveness of visual representation to assist physicist in identifying possible temporal association among complex and chaotic atom movements in ion trajectories

A Multitier Deep Learning Model for Arrhythmia Detection

Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework