A touch to a broad topic: The role of intercultural communication in sharing ideas and experiences among students

В статье рассматриваются вопросы взаимоотношений между студентами из различных культур с точки зрения социальных, образовательных и культурных аспектов. Особое внимание уделяется вопросам развития компетенций в рамках университетского образования, а также вопросам преодоления культурных и социальных проблем в образовательной среде

Students intention towards entrepreneurship to participate in development of regional economy hot to be burden on economy

Entrepreneurship alarming researchers and policy makers, to involve jobs seekers students in entrepreneurship after graduation to handle the youth unemployment. This paper tried to explain the involvement of students in regional entrepreneurship and some factors which were studied by most of the researchers, and were found to have significant effect on students to be entrepreneur in the near future, which include university environment and social support

The effects of internship programs and course design on graduates’ employability skills

The degree is no longer enough, competition is increasing day by day among new applicants who are searching for the jobs. Every company needs well knowledgeable and skillful workforce. Some employers especially looking for technical experts and organizational experienced employees in the current competitive and technological market. The overload graduates’ availability in the market made it easy for the companies to select the cream from the large pool of candidates. This situation made the chances of getting job more difficult for those students who are just earning degrees in traditional way without any experience and technical expertise. A lot of work has been done in literature regarding this issue how to made it possible for the graduates to be employable in the market. The researchers in the literature mostly focused on the requirements of the jobs available in the markets and the applicants’ suitability for that jobs. This paper presents an attempt to explains the impact of internship programs and course design on students’ employability skills. The findings may increase the chances of selection easy in getting jobs for graduates and additional information to universities in preparing students according to the requirements of the job markets

Real-time power quality detection and classification system

The increasing number of power electronics equipment contributes to the poor quality of electrical power supply and has become a vital concern to electricity users at all levels of usage. The power quality signals can affect manufacturing process, malfunction of equipment and economic losses. Thus, it is necessary to detect and classify different kind of power quality signals for rectify failures and ensure quality of power line signal. This research presents the analysis power quality signals using time-frequency distributions (TFDs) which are spectrogram, Gabor transform and S-transform for signals detection and classification. Since the signals consist of multi-frequency components and magnitude variation, the TFDs are very appropriate to be used that represent the signals, jointly, in time-frequency representation (TFR). From the TFR, parameters of the signals are estimated and then are used to identify the characteristics of the signals. Referring to IEEE Std. 1159-2009, the signal characteristics are obtained and then served as the input for signal classifier to classify power quality signals. Based on the analysis, the best TFD is identified in terms of accuracy of the signal characteristics, memory size and computation complexity of data processing and chosen for power quality signals detection and classification system. By simulating in MATLAB, the performance of the classification system is verified by generating and classifying 100 signals with various characteristics for each type of power quality signals. In addition, the system is also tested using 100 real signals which were recorded from a power line. The results show that, S-transform is the best TFD and the classification system gives 100 percent correct classification for all power quality signals. For the real signals, the system also presents 100 percent correct classification. Thus, the outcome of this research shows that the system is very appropriate to be implemented for power quality monitoring system

A Case Study on Puspamara Sendirian Berhad

Bearish stock market, depreciation of currency, down-sizing of companies and other economic difficulties have resulted in a violence and pure challenging bussiness environment. These factors awakened the necessity of proper and organized planning and strategy for the company's current and coming survival. Puspamara Sdn Bhd, is textile company in Petaling Jaya, managing production of garments, apparel, design uniform and other product and services. The company experienced a decline in profitability with a 41% decrease from 1996. The inexistence of proper strategic goals and long-term planning were viewed to result to the instability of the company performance and future business continuity. This study was done in order to derive strategic alternatives and decisions to enable Puspamara Sdn. Bhd. to overcome the difficulties and eliminate further unsatisfactory outcome in the future especially in the globalization world. The study analyzed the external and internal environment and generic the strategic alternatives using the SWOT matrix and SPACE determinants model

A New Two Points Method for Identify Dominant Harmonic Disturbance Using Frequency and Phase Spectrogram

This paper is focused on a practical new method for dominant harmonic disturbance detection implemented using phase and frequency spectrogram based on two-point method. The first measurement point is measured at the incoming of the point of common coupling while the second measurement point at the incoming of the load. After that, the data is processed with phase and frequency spectrogram. By comparing the data, the dominant harmonic disturbance can be identified clearly. The proposed method is compared with power direction method which is the earliest method normally used in commercial product. Then, simulation and experiment are conducted to verify the accuracy of the proposed method. Finally, the results show the proposed method is more accurate than power direction method. Further work is needed to investigate the performance of the proposed method by field measurement

Voltage Source Inverter Fault Detection System using Time Frequency Distribution

Open-switch and short-switch in a three-phase voltage source inverter (VSI) have a possibility to fault due to problems of switching devices.Any failure of the system in these applications may incur a cost and risk human live. Therefore, knowledgeon the fault mode behaviour of an inverter is extremely important from the standpoint of system design improvement, protection and fault detection. This paper presents detailed simulation results on condition monitoring and fault behaviour of VSI. The results obtained from the developed monitoring system allows user to identify the fault current. The developed system showed the capability in detecting the performance of VSI as well as identifying the characteristics of type of faults. This system provides a precaution and early detection of fault, thus reduces high maintenance cost and prevent critical fault from happening

MONITORING VEGETATION DENSITY USING SPECTRAL VEGETATION INDICES: A CASE STUDY OF MALAM JABBA REGION, DISTRICT SWAT, PAKISTAN

The limited forest resources with a higher deforestation rate per annum, Pakistan ranks the second highest in Asia. FAO reported that the annual forest cover change rate during 1990–2000 was −1.8% and increased to −2.2 % between 2000–2010. Most of Pakistan's total forest resources, dominantly natural forest, are situated in the Northern regions. Stepping into the corridor of the 21st century, the Spatio-temporal analysis has been evolved using Satellite Remote Sensing data aided with Geographic Information System) GIS) platforms. The study is carried out over the mountainous vegetation land of Malam Jabba, district Swat, Khyber Pakhtunkhwa, Pakistan. Due to varying topography and the region being part of the agro-forestry zone, drastic changes were observed in vegetation and built-up areas. The vegetation cover has been identified and classified based on elevation throughout the area. This study has provided essential insights into vegetation cover change over a period of four decades. Vegetation cover is classified into high to very high, medium, and low to very low. The Landsat and the SRTM DEM satellite imageries were exported to the ERDAS software for pre-and post-processing, and for further analysis ArcGIS 10.5 was used, where the vegetation density change for each period was computed from the pixels by using vegetation indices like VCI, NDVI, and SAVI. The results show a significant decline from 1980 to 2010 in vegetation density in the Northwestern direction; however, an increasing trend can be seen in 2020 due to awareness and the Government’s Billion Tree Tsunami initiative. Such studies can significantly benefit researchers and decision-makers interested in satellite remote sensing for forest and other vegetation cover monitoring and management at a regional scale

Power Quality Signals Classification System Using Time-Frequency Distribution

Power quality signals are an important issue to electricity consumers. The signals will affect manufacturing process, malfunction of equipment and economic losses. Thus, an automated monitoring system is required to identify and classify the signals for diagnosis purposes. This paper presents the development of power quality signals classification system using time-frequency analysis technique which is spectrogram. From the time-frequency representation (TFR), parameters of the signal are estimated to identify the characteristics of the signals. The signal parameters are instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion, total harmonic distortion and total non harmonic distortion. In this paper, major power quality signals are focused based on IEEE Std. 1159-2009 such as swell, sag, interruption, harmonic, interharmonic, and transient. An automated signal classification system using spectrogram is developed to identify, classify as well as provide the information of the signal

Lead Acid Battery Analysis using Spectrogram

Battery is an alternative option that can be substituted for future energy demand. Numerous type of battery is used in industries to propel portable power and its makes the task of selecting the right battery type is crucial. These papers discuss the implementation of linear timefrequency distribution (TFD) in analysing lead acid battery signals. The time-frequency analysis technique selected is spectrogram. Based on, the time-frequency representations (TFR) obtain, the signal parameter such as instantaneous root mean square (RMS) voltage, direct current voltage (VDC) and alternating current voltage (VAC) are estimated. The parameter is essential in identifying signal characteristics. This analysis is focussing on lead-acid battery with nominal battery voltage of 6 and 12V and storage capacity from 5 until 50Ah, respectively. The results show that spectrogram technique is capable to estimate and identify the signal characteristics of Lead Acid battery