A PERISHABLE INVENTORY MODEL WITH UNKNOWN TIME HORIZON

Traditionally, the time (planning) horizon over which the inventory for a particular item will be controlled is often assumed to be known (finite or infinite) and the total inventory cost is usually obtained by summing up the cost over the entire time horizon. However, in some inventory situations the period over which the inventory will be controlled are difficult to predict with certainty, as the inventory problems may not live up to or live beyond the assumed planning horizon, thereby affecting the optimality of the model. This paper presents a deterministic perishable inventory model for items with linear trend in demand and constant deterioration when time horizon is unknown, unspecified or unbounded. The heuristic model obtains replenishment policy by determining the ordering schedule to minimize the total cost per unit time over the duration of each schedule. A numerical example and sensitivity analysis are given to illustrate the model

A computer-based vision systems for automatic identification of plant species using kNN and genetic PCA

Precision farming involves integration of different areas of disciplines to lower production costs and improve productivity. One major arm of precision farming or agriculture is the development of computer - based vision systems for automatic identification of plant species. This work involves application of k Nearest Neighbour (kNN) and genetic principal component analysis (GA - PCA) for the development of computer - based vision systems for automatic identification of plant species. As the first contribution, several image descriptors were extracted from the images of plants found in the Flavia data set. Lots of these image features are affine maps and amalgamation of such massive features in one study is a novel idea. These descriptors are Zernike Moments (ZM), Fourier Descriptors (FDs), Lengendre Moments (LM) Hu 7 Moments, Texture, Geometrical properties and colour features. The GA - PCA (1907 x 41) feature space improved the classification accuracy of kNN from 84.98% to 88.75%

Multiyear Analysis of Ground-Based Sunphotometer (AERONET) Aerosol Optical Properties and Its Comparison with Satellite Observations over West Africa

The Sahelian West Africa Long 20W 20E Lat 0 30N by its climatological and geographical conditions is a key region for the characterization of global atmospheric aerosol optical properties This study evaluates the spatial and temporal variation of the Aerosol Optical Depth AOD440nm aerosol particle size characterization Angstrom exponent 440-675nm at four locations Agoufou Banizoumbou Cape Verde and Ilorin over a period of January 2005 to December 2009 Results of the day-to-day AOD440nm variations as well as the seasonal and annual variations are presented in order to establish the aerosol climatology in the region We compared satellite derived data of Total Ozone Mapping Spectrometer - Aerosol Index TOMSAI MODIS Terra and Aqua with those of ground-based Sunphotometer AERONET measurements In general there exits good relationship between MODIS Terra and Aqua and the ground-based AERONET measurements with correlation coefficients R2 0 8 reported in all stations However low coefficients as low as 0 40 were obtained in all the stations for regressions between TOMS AI and ground-based Sunphotometer AERONET dat

Age-related Macular Degeneration: Current concepts in pathogenesis and management

Age-related macular degeneration, which was once thought to be a disease mainly found in Caucasian populations in Europe and America, is now also appearing more frequently among non-white populations in the developing world. Ophthalmic practitioners should be aware of this. This paper reviews current concepts in the pathogenesis and management of agerelated macular degeneration as found in Pubmed journals over the past ten years with a view to recommending optimal treatment regimes for African populations. Keywords: age-related macular degeneration, pathogenesis, genetics, management, antiVEGFNigerian Journal of Ophthalmology Vol. 16 (1) 2008: pp. 5-1

Best Macular Dystrophy in a Nigerian: A Case Report

Best macular dystrophy is reported to be rare in Africans. It is a hereditary disease that starts in childhood and progresses through some stages before visual symptoms occur. This case report presents a 43-year-old Nigerian with the disease and stresses the importance of regular eye exams of patients and relatives to detect changes such as choroidal neovascular membrane amenable to treatment

A neuro-genetic hybrid approach to automatic identification of plant leaves

Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds. In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification. This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves. A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection. This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented

The Image Bank: Reflections on an Incomplete Archive

This thesis examines the development of a digital archive for The Image Bank at GSU as a process of excavation and reconstruction. It defines the digital archive as a medium for the institutionalization of knowledge, its reproduction, and preservation. In addition, this thesis examines the digital archive as it operates on a continuum of materiality and immateriality, encompassing fractured distinctions between its possibilities and impossibilities in an increasingly dematerialized digitized landscape

Measurement of Atmospheric Concentration of CO2 in the Hudson Bay Lowlands: An Application of a Lagrangian Particle Dispersion Model (STILT)

Atmospheric CO2 concentrations are influenced by surface fluxes, as well as advection and vertical mixing on the way to the measurement tower. The capability of transport models to accurately represent air parcel trajectories and footprints is crucial in inverse analysis. This study employs the Stochastic Time-inverted Lagrangian Transport model (STILT), driven by meteorological inputs from the North American Regional Reanalysis (NARR), to simulate atmospheric CO2 in the Hudson Bay Lowlands. The primary objectives include: (1) Characterize daily, seasonal and interannual variations of atmospheric CO2 for a 5-year (2008-2012) period; (2) Evaluate the performance of the STILT model, and CarbonTracker flux estimates. STILT-modelled CO2 concentrations compare reasonably against observations. The mean model bias was -0.57 ppm at Churchill, and -2.44 ppm at Fraserdale. Smoothed seasonal curves fitted to the daily afternoon data revealed that model bias was highest during summertime, particularly over the Fraserdale region. This disparity between modelled and observed results are attributed to transport errors related to advection and PBL mixing