Inventory Model with Seasonal Demand: A Specific Application to Haute Couture

In the stochastic multiperiod inventory problem, a vast majority of the literature deals with demand volume uncertainty. Other dimensions of uncertainty have generally been overlooked. In this paper, we develop a newsboy formulation for the aggregate multiperiod inventory problem intended for products of short sales season and without replenishments. A distinguishing characteristic of our formulation is that it takes a time dimension of demand uncertainty into account. The proposed model is particularly suitable for applications in haute couture, i.e., high fashion industry. The model determines the time of switching primary sales effort from one season to the next as well as optimal order quantity for each season with the objective of maximizing expected profit over the planning horizon. We also derive the optimality conditions for the time of switching primary sales effort and order quantity. Furthermore, we show that if time uncertainty and volume uncertainty are independent, order quantity becomes the main decision over the interval of the primary selling season. Finally, we demonstrate that the results from the two-season case can be directly extended to the multi-season case and the limited resource multiple-item case

Stochastic Model Considering Individual Satellite Signal Quality on GPS Positioning

Simple stochastic model is normally used in GPS positioning by making assumption that all GPS observables are statistical independent and of the same quality. By the above assumption, similar variance is assigned indiscriminately to all of the measurements. A more detail stochastic model considering specific effects affecting each observable individually may be approached such as the ionospheric effect. These effects relate to phase and amplitude measurements in satellite signals that occur due to diffraction on electron density in the ionosphere. This is particularly relevant to those regions frequent with active ionospheric event such as equatorial and high latitude regions. A modified stochastic model considering individual satellite signal quality has been implemented which based on the computation of weights for each observable. The methodology to account for these effects in the stochastic model are described and results of experiments where GPS data were processed in relative positioning mode is presented and discussed. Two weighting parameters have been used in the experiment: elevation angles and tracking error variance in the GPS receiver. The results have shown improvement of 10.3% using the elevation angles as weighting parameters and 11.5% using the tracking error variance as weighting parameter

Global positioning system (GPS) positioning errors modeling using Global Ionospheric Scintillation Model (GISM)

As technology advancement progresses throughout the years in this modern age, every technology has its part to play in that the world is moving towards a brighter future. GPS (Global Positioning System) has diverse application in current globalized world, its application has pervasive benefits not only to navigation and positioning, it is pivotal in industries like logistics, shipping, financial services and agriculture. Since the decision to shut down the Selectivity Availability (SA) by former U.S. President, Bill Clinton, ionospheric effect is now the primary concern of error contributing factors in GPS. Ionospheric scintillation induces rapid fluctuations in the phase and the amplitude of received GNSS signals. These rapid fluctuations or scintillation potentially introduce cycle slips, degrade range measurements, and if severe enough lead to loss of lock in phase and code. Global Ionospheric Scintillation Model (GISM) was used to compute amplitude scintillation parameter for each GPS satellite visible from Melaka, Malaysia (Latitude 2° 14' N, Longitude 102° 16' E) as its location has strong equatorial scintillation behavior. The output data from GISM was then used to calculate the positioning error. There are two schemes that were used. First, the positioning error was calculated for all the visible satellites. Secondly, the positioning error was calculated for those satellite that have amplitude scintillation index, S4 <;0.7. Comparison of results from the both schemes was then made

NOAA Weather Satellite Station at KUTKM

A ground station has been installed at Kolej Universiti Teknikal Kebangsaan Malaysia (KUTKM) to receive the VHF signal form the United State National Oceanographic and Atmospheric Administration (NOAA) Low Earth Orbiting Satellite (LEO) weather satellite series. The satellite signal was received and decoded as image which was displayed on computer screen in the form of visible light, infra-red or the combination of both. The image file is then processed and stored into a local computer for meteorology study in KUTKM

Global Positioning System (GPS) Positioning Errors During Ionospheric Scintillation Event

As technology advancement progresses throughout the years in this modern age, every technology has its part to play in that the world is moving towards a brighter future. GPS (Global Positioning System) has diverse application in current globalized world, its application has pervasive benefits not only to navigation and positioning, it is pivotal in industries like logistics, shipping, financial services and agriculture. Since the decision to shut down the Selectivity Availability (SA) by former U.S. President, Bill Clinton, ionospheric effect is now the primary concern of error contributing factors in GPS. Ionospheric scintillation induces rapid fluctuations in the phase and the amplitude of received Global Navigation Satellite System (GNSS) signals. These rapid fluctuations or scintillation potentially introduce cycle slips, degrade range measurements, and if severe enough lead to loss of lock in phase and code. Global Ionospheric Scintillation Model (GISM) was used to compute amplitude scintillation parameter for each GPS satellite visible from Melaka, Malaysia (Latitude 20 14’ N, Longitude 1020 16’ E) as its location has strong equatorial scintillation behavior. The output data from GISM was then used to calculate the positioning error where it is depends on the Dilution of Precision (DOP) and User Equivalent Range Error (UERE). There are two schemes that were used. First, the positioning error was calculated for all the visible satellites with better DOP but worse UERE due to scintillation event. Secondly, the positioning error was calculated for those satellites that have amplitude scintillation index, S4 < 0.7 which leads to worse DOP with better UERE. Comparison of results from the both schemes was then made

Designing Cluster Plots for Sampling Local Plant Species Composition for Biodiversity Management

Aim of study: Cluster plot designs are widely used in national forest inventory systems to assess current forest resources. By spreading subplots apart, a cluster plot could potentially capture a large variety of local plant species. This aspect has rarely been examined in the past. This study is conducted to understand how design factors of a cluster plot affect estimates of local plant species composition.Area of study: Two large census forest plots in Taiwan and Peninsular Malaysia over 25 ha with different species richness were used.Material and methods: Design factors of a cluster plot were plot configuration (PCONFIG), plot area (PAREA), cluster layout (CLAYOUT), and extent of ground area covered by a cluster (CEXTENT). Jaccard and Sørensen similarity indices were used to compare species compositional similarity between two cluster plot designs. A simulation study was carried out.Main results: Results were consistent among the study sites and similarity indices. PAREA, CLAYOUT, and CEXTENT notably influenced how species composition was sampled. Larger PAREA increased similarity in species composition between two cluster plot designs. Square and rectangle CLAYOUT had the most dissimilar species composition between them. Larger CEXTENT decreased similarity in species composition.Research highlights: We recommend that for CEXTENT ≤ 1000 m2 and PAREA ≤ 500 m2, a cluster plot of rectangle CLAYOUT is preferred for information gain. The study could potentially benefit forest managers designing cluster plots for plant diversity assessment.Keywords: Biodiversity assessment; composition similarity; national forest inventory; species diversity; sampling design; sampling efficiency.Abbreviation used: extent of ground area covered by a cluster (CEXTENT); cluster layout (CLAYOUT); Jaccard similarity index (JAC); plot area (PAREA); plot configuration (PCONFIG); Sørensen similarity index (SOR)

REGULATION OF 15-LIPOXYGENASE AND CHARACTERISATION OF THE LIPOXIN/RESOLVIN RECEPTOR

Master'sMASTER OF SCIENC

Solar irradiance forecasting for Malaysia using multiple regression and artificial neural network

The installed capacity of solar photovoltaic (PV) globally continues to rise. In Malaysia, the monthly average daily solar radiation is 4,000-5,000 Wh/m², with the average daily sunshine duration ranging from 4 to 8 h. However, the output of solar energy is related to solar irradiance, which lacks stability due to weather variation. Therefore, solar irradiance forecasting has become an important resource for network grid operators to control the output of solar PV energy. Weather forecasting data, such as temperature, dew point, humidity, pressure and wind speed, are widely available from local meteorological organisations. However, solar irradiance forecasting data is often unavailable. In this paper, multiple regression (MR) and artificial neural network (ANN) models are used to forecast solar irradiance using weather forecasting data. The correlation of each weather parameter with solar irradiance is investigated. It is evident that the ANN model is able to improve the accuracy in terms of root mean square error (RMSE) by 18.42% of its as compared to the MR mode

On the spore ornamentation of the microsoroid ferns (microsoroideae, polypodiaceae)

Microsoroideae is the third largest of the six subfamilies of Polypodiaceae, containing over 180 species. These ferns are widely distributed in the tropical and subtropical regions of the Old World and Oceania. We documented the spore ornamentation and integrated these data into the latest phylogenetic hypotheses, including a sampling of 100 taxa representing each of 17 major lineages of microsoroid ferns. This enabled us to reconstruct the ancestral states of the spore morphology. The results show verrucate ornamentation as an ancestral state for Goniophlebieae and Lecanoptereae, globular for Microsoreae, and rugulate surface for Lepisoreae. In addition, spore ornamentation can be used to distinguish certain clades of the microsoroid ferns. Among all five tribes, Lecanoptereae show most diversity in spore surface ornamentation.Peer reviewe