Relationship of Strategic Management Practices and Performance of Percetakan Nasional (M) Berhad

This paper discusses mainly about strategic management and its implications especially in the organization. The paper aims to develop an understanding of the role of strategic management in the proper allocation of business resources on businesses today especially in the global business environment. This study's objective is to present the importance of having the right organization that fits to the strategy of a company. It aims to determine the effect of the organization to the implementation of a strategy especially a global strategy that can bring changes to the production process, distribution and marketing channel that will demand new ways of organization. This study was done among 200 exempt staffs in Percetakan Nasional Malaysia Berhad. Data were gathered through questionnaires and was being analyzed by using Statistical Package for Social Science (SPSS) version 19

Effects of soil variation between peat soil and mineral soil on fruit quality of pineapple (Ananas Comosus) cultivars: N36 and josapine

Consumption of pineapple is predicted to increase in the near future in which the demand for continuous supply of the fruit becomes of major interest. This research is focused on finding the influence of growth medium between peat soil and mineral soil to the weight and production of vitamin C and sugar contents of the pineapple fruits. Two cultivars were investigated: N36 and Josapine. The vitamin C is quantified by Iodate Titration and sugar content is analyzed by Digital Refractometer. The chemical soil properties were analyzed by using atomic absorption spectrophotometer at the Soil Analysis Section laboratory from Department Of Agriculture, Malaysia. All data were analyzed statistically through ANOVA by using SPSS. Results indicated that pineapple planted on peat soil produced heavier fruits than those in mineral soil. N36 pineapple reports no significant difference (p < 0.05) of the vitamin C content and the sweetness between different cultivation soil types. However, Josapine pineapples cultivated on mineral soil (FIMA Plantation) produce significantly 115% higher vitamin C content (19.243 ± 7.191 mg∙100g-1), and significantly 30% lower sugar content (5.403 ± 1.721% °Bx) than cultivated on peat soil (Peninsula Plantations). The finding of the soil type’s influence on the fruit quality production is beneficial for pineapple producers and researchers to broaden up the development of pineapple-based products in the long run. The promotion of pineapple nutritional contents is beneficial to increase consumers’ knowledge and awareness on the importance of pineapple consumption especially in Asian regions

Wide Band Open Ended Air Gap RLSA Antenna at 5.8GHz Frequency Band

RLSA antenna is a popular candidate for the application of Point to Point Microwave Link. This is due to its capability of carrying high speed signal. RLSA prototypes has been designed and developed at the frequency range of 5725 – 5875 MHz by few researchers. The classic design was using enclose air gap as a separator between radiation surface and ground plane. Then, the slow wave material has been used in the RLSA cavity. The implementation of FR4 board to RLSA design recently has allowed a reconfigurable and beam shaping of radiation pattern.[5]. The open air gap cavity structure normally implemented in broadband planar antenna.[6] This structure normally provide a wide bandwidth and good return loss on the desired frequency. Therefore, an investigation of this technique and hybrid with FR4 board as a cavity material will be very interesting due to condition of easy to manufacture, lighter the antenna weight and durable.[7

Radial Line Slot Array Antenna Development in Malaysia

The Radial Line Slot Array (RLSA) antennas become more popular due to its good characteristics such as low profile, low cost, aesthetically pleasing, ease of installation and simple structure. Many researchers have contributed to the development of RLSA antennas. This antenna started with the name of annular slot antenna designed by Kelly and Goebel in 1960’s. In 1980’s, the researchers from Japan have innovated this idea and proposed a new concept of radial slot planar antenna names Radial Line Slot Antenna. From here, many researches have been carried out involving researchers around the world. Researchers from Malaysia also involves in the development of RLSA antenna. The RLSA antenna research in Malaysia focuses on application that suitable to RLSA characteristic. Those applications are Direct Broadcast Satellite Reception, Point to point Microwave Link for Fixed Broadband Wireless Access and RFID. This paper will be focus on RLSA antenna development started with the history, research outcomes and the research focus in Malaysia

A comparison study of deep CNN models for brain tumor MRI image classification

Throughout the life journey, brain health conditions may emerge and are identified by disruptions in normal brain growth and brain functioning. One of the disruption that may manifest as a neurological condition is brain tumor, which incidence is increasing in recent years. Since manual method of classifying brain tumor images is tedious and can only be done at certain diagnostic centers, an alternative way of using deep learning technique to detect abnormal tissues in the brain is undertaken in this work. In this project, different architectures of Convolutional Neural Network (CNN) models namely AlexNet, Residual Network-18 (ResNet-18) and GoogLeNet were adopted and compared for their performance in classification of brain tumor Magnetic Resonance Imaging (MRI) images via two different approaches namely transfer learning method and modified method. The performance of the CNN models were evaluated and the most reliable model for the classification of brain tumor MRI images was determined. From the comparison done between the transfer learning method and modified method, transfer learning AlexNet showed the highest accuracy of 87.11% while modified ResNet-18 demonstrated the lowest accuracy of 76.09%. Based on observation of this project results and findings from other sources, for both approaches taken for brain tumor image classification, the tuning of hyperparameters used in training options (for transfer learning method only), the variation of image data augmentation to avoid overfitting and the depth of CNN topology influences the accuracy of the CNN models. The best CNN model for the classification of brain tumor MRI images with great accuracy was proven to be transfer learning AlexNet model