Elliptic curve cryptosystems (ECC) provides better security for each bit key utilized compared to the RSA cryptosystem. For this reason, it is projected to have more practical usage than the RSA. In ECC, scalar multiplication (or point multiplication) is the dominant operation, namely, computing nP from a point P on an elliptic curve, where n is an integer defined as the point resulting from adding P + P + ... + P, n times. However, for practical uses, it is very important to improve the efficiency of the scalar multiplication. Solinas (1997) proposes that the τ-adic Non-Adjacent Form (τ-NAF) is one of the most efficient algorithms used to compute scalar multiplications on Anomalous Binary curves. In this paper, we give a new property (i.e., Theorem 1.2) of τ-NAF(n) representation for every length, l. This is useful for evaluating the maximum and minimum norms occurring among all length-l elements of Z(τ). We also propose a new cryptographic method by using randomization of a multiplier n to nr an element of Z(τ). It is based on τ-NAF. We focused on estimating the length of RTNAF(nr) expansion by using a new method.
Linear array of permutations is hard to be factorised. However, by using a starter set, the process of listing the permutations becomes easy. Once the starter sets are obtained, the circular and reverse of circular operations are easily employed to produce distinct permutations from each starter set. However, a problem arises when the equivalence starter sets generate similar permutations and, therefore, willneed to be discarded. In this paper, a new recursive strategy is proposed to generate starter sets that will not incur equivalence by circular operation. Computational advantages are presented that compare the results obtained by the new algorithm with those obtained using two other existing methods. The result indicates that the new algorithm is faster than the other two in time execution.
A multivariate control chart is a common tool used for monitoring and controlling a process whose quality is determined by several related variables. The objective of this study is to compare the performances of the multivariate exponentially weighted moving average (MEWMA) and the multivariate synthetic T2 control charts, for the case of a multivariate normally distributed process. A comparative study is made based on the average run length (ARL) performances of the control charts, using the simulation method, in order to identify the chart having the best performance in monitoring the process mean vector. The performances of the two charts, for different sample sizes and correlation coefficients, are presented in this paper. It was found that the MEWMA chart outperformed synthetic T2 chart for small shifts but the latter prevailed for moderate shifts. Both charts performed equally well for larger shifts. In addition, the performances of both MEWMA and synthetic T2 charts were found to be influenced by sample size and correlation coefficient. The two charts’ performances improved as the sample size and correlation coefficient increased for small and moderate shifts, but the charts’ performances did not depend on sample size and correlation coefficient when the shift was large.
In this modern world, Information Technology gives impacts on society, countries, economy, and environment. This paper discusses the positive, negative, direct and indirect impacts of IT on environmental issues. A strategy for sustainable development in ICT and its future demand are also proposed. Apart from that, a research was also done to find a quantitative indicator to show the relationship between demand in IT industry and impacts to the environment. By using a mathematical formula, an estimation of the effect to the environment can be found. By using the indicator, it is hoped that society and the IT industry will become more aware of their action to the environment.
We had reviewed the current practice in stocks market analysis where stock is represented by its closing price, and then found that this approach may be misleading. In actuality, in the daily activity of stocks market, stock is represented by four prices, namely opening, highest, lowest, and closing prices. Thus, stock is a multivariate time series of those four prices and not a univariate time series of closing price only. In this paper all four prices will be considered. Then, the notion of multivariate time series similarity among stocks will be developed as a generalisation of univariate time series similarity. The results are used to construct stocks network in multivariate setting. To filter the economic information contained in that network, the standard tools in econophysics is used. Furthermore, the topological properties of stocks are analysed by using the most common centrality measures. As an example, Bursa Malaysia data are investigated and we show that the proposed approach can better figure out the real situation compared to the current one.
In this Viewpoint, the impact of the paper published by Gautam R. Desiraju and Angelo Gavezzotti (J. Chem. Soc., Chem. Commun., 1989, 621) upon the development of Crystal Engineering, now recognised a key discipline in contemporary chemical/pharmaceutical/materials science, is discussed.
Clinicians, working in the health-care diagnostic systems of developing countries, currently face the challenges of rising costs, increased number of patient visits, and limited resources. A significant trend is using low-cost substrates to develop microfluidic devices for diagnostic purposes. Various fabrication techniques, materials, and detection methods have been explored to develop these devices. Microfluidic paper-based analytical devices (μPADs) have gained attention for sensing multiplex analytes, confirming diagnostic test results, rapid sample analysis, and reducing the volume of samples and analytical reagents. μPADs, which can provide accurate and reliable direct measurement without sample pretreatment, can reduce patient medical burden and yield rapid test results, aiding physicians in choosing appropriate treatment. The objectives of this review are to provide an overview of the strategies used for developing paper-based sensors with enhanced analytical performances and to discuss the current challenges, limitations, advantages, disadvantages, and future prospects of paper-based microfluidic platforms in clinical diagnostics. μPADs, with validated and justified analytical performances, can potentially improve the quality of life by providing inexpensive, rapid, portable, biodegradable, and reliable diagnostics.
Although a significant amount of brewery wastewater (BW) is generated during beer production, the nutrients in the BW could be reused as a potential bio-resource for biohydrogen production. Therefore, improvements in photofermentative biohydrogen production due to a combination of BW and pulp and paper mill effluent (PPME) as a mixed production medium were investigated comprehensively in this study. The experimental results showed that both the biohydrogen yield and the chemical oxygen demand removal were improved through the combination of BW and PPME. The best biohydrogen yield of 0.69 mol H2/L medium was obtained using the combination of 10 % BW + 90 % PPME (10B90P), while the reuse of the wastewater alone (100 % BW and 100 % PPME) resulted in 42.3 and 44.0 % less biohydrogen yields than the highest yield, respectively. The greatest light efficiency was 1.97 % and was also achieved using the combination of both wastewaters at 10B90P. This study revealed the potential of reusing and combining two different effluents together, in which the combination of BW and PPME improved the nutrients and light penetration into the mixed production medium.
Recovery of cellulose fibres from paper mill effluent has been studied using common polysaccharides or biopolymers such as Guar gum, Xanthan gum and Locust bean gum as flocculent. Guar gum is commonly used in sizing paper and routinely used in paper making. The results have been compared with the performance of alum, which is a common coagulant and a key ingredient of the paper industry. Guar gum recovered about 3.86mg/L of fibre and was most effective among the biopolymers. Settling velocity distribution curves demonstrated that Guar gum was able to settle the fibres faster than the other biopolymers; however, alum displayed the highest particle removal rate than all the biopolymers at any of the settling velocities. Alum, Guar gum, Xanthan gum and Locust bean gum removed 97.46%, 94.68%, 92.39% and 92.46% turbidity of raw effluent at a settling velocity of 0.5cm/min, respectively. The conditions for obtaining the lowest sludge volume index such as pH, dose and mixing speed were optimised for guar gum which was the most effective among the biopolymers. Response surface methodology was used to design all experiments, and an optimum operational setting was proposed. The test results indicate similar performance of alum and Guar gum in terms of floc settling velocities and sludge volume index. Since Guar gum is a plant derived natural substance, it is environmentally benign and offers a green treatment option to the paper mills for pulp recycling.
The study reports on the preparation of cellulose nanocrystals (CNCs) from wastepaper, as an environmental friendly approach of source material, which can be a high availability and low-cost precursor for cellulose nanomaterial processing. Alkali and bleaching treatments were employed for the extraction of cellulose particles followed by controlled-conditions of acid hydrolysis for the isolation of CNCs. Attenuated total reflectance Fourier Transform Infrared (ATR FTIR) spectroscopy was used to analyze the cellulose particles extracted while Transmission electron microscopy images confirmed the presence of CNCs. The diameters of CNCs are in the range of 3-10nm with a length of 100-300nm while a crystallinity index of 75.9% was determined from X-ray diffraction analysis. The synthesis of this high aspect ratio of CNCs paves the way toward alternative reuse of wastepaper in the production of CNCs.
Data mining processes such as clustering, classification, regression and outlier detection are developed based on similarity between two objects. Data mining processes of categorical data is found to be most challenging. Earlier similarity measures are context-free. In recent years, researchers have come up with context-sensitive similarity measure based on the relationships of objects. This paper provides an in-depth review of context-based similarity measures. Descriptions of algorithm for four context-based similarity measure, namely Association-based similarity measure, DILCA, CBDL and the hybrid context-based similarity measure, are described. Advantages and limitations of each context-based similarity measure are identified and explained. Context-based similarity measure is highly recommended for data-mining tasks for categorical data. The findings of this paper will help data miners in choosing appropriate similarity measures to achieve more accurate classification or clustering results.
In the study of disease mapping, relative risk estimation is the focus of analysis. Many methods have
been introduced to estimate relative risk. In this paper, one of the common spatial models known as
Besag, York and Mollié (BYM) model is discussed, and its application to dengue data for epidemiology
weeks 1 to 52 of the year 2013 for 16 states in Malaysia is studied. Findings show that Selangor has the highest relative risk of dengue in comparison with other states. Data on the estimated relative risks are presented in the form of risk maps which can be used as a tool for the prevention and control of dengue.
In this paper, cap and pin porcelain insulator was studied under an environment with different levels
of humidity. The electric field strength and voltage distribution profile along the insulator string was
simulated using a computational software package. In this study, ANSYS Maxwell based on the Finite
Element Method (FEM) was used to simulate the short standard insulator string. The short standard
insulator string was modelled as a five-unit cap and pin porcelain insulator that was stacked according
to the IEC 60383 standard. Different humidity levels measured using relative humidity is applied to
the insulator. From this simulation, the locations within the insulator under high electric field stress are
identified when different humidity is applied.
The development of Transparent Solar Cells (TSC) has become the main focus of solar energy research in recent years. The TSC has a number of applications and make use of unexploited space such as skyscraper windows. In this paper, TSC is fabricated using commercially available titanium dioxide (TiO2) P25 to make a paste, which is deposited on FTO glass using screen printing and spin coating methods. The effects of the thickness of the TiO2 film on transparency are examined. The paste is synthesised in the Cleanroom and used in both methods of deposition. The final cell fabrication is a Dye sensitised solar cell (DSSC). The obtained transparency of the FTO glass is 83%, and after the deposition of TiO2 it is reduced to less than 80%. The overall transparency of the DSSC, which was made using the spin coating method, is 70% with an Isc of 9.5 mA and Voc 853mV.
Preventive tests and diagnosis of in-service power transformer are important for early fault prediction and increased reliability of electricity supply. However, some existing diagnostic techniques require transformer outage before the measurement can be performed and need expert knowledge and experiences to interpret the measurement results. Other measurement techniques such as chemical analyses of insulating oil may cause significant variance to measurement results due to different practices in oil sampling, storage, handling and transportation of oil. A cost-effective measuring technique, which is simple, providing fast and an accurate measurement results, is therefore highly required. The extended application of Polarisation and Depolarisation (PDC) measurement for characterisation of different faults conditions in-service power transformer has been presented in this paper. Earlier studies on polarisation and depolarisation current of oil samples from in-service power transformer shows that depolarisation has provided significant information about the change of material properties due to faults in power transformer. In this paper, a new approach based on Depolarisation Current Ratio Index (DRI) was developed for identifying and classifying different transformer fault conditions. The DRI at time interval of 4s to 100s was analysed and the results show that DRI of depolarisation current between 5/100s and 10/100s provides higher correlation on the incipient faults in power transformer.
One of the concerns in power system preventive control and security assessment is to find the point where the voltage and frequency collapse and when the system forces a severe disturbance. Identifying the weakest bus in a power system is an essential aspect of planning, optimising and post-event analysing procedures. This paper proposes an approach to identify the weakest bus from the frequency security viewpoint. The transient frequency deviation index for the individual buses is used as the weakest bus identification as well as a frequency security indicator. This approach will help to determine the bus with the worst deviation, which helps to analyse the system disturbance, takes proper control action to prevent frequency failure, and most importantly, observes consumer frequency. The approach is applied to the WSCC 9 bus test system to show the feasibility of the method.
This paper provides a qualitative overview of different Optical Fiber Sensors (OFS),
which play important role in the field of sensors due to their excellent
characteristics, spontaneous response and easy handling system. The current
state of the art of optical fiber technology is reviewed, namely based on its main
characteristics and sensing advantages. In addition, the working principle of OFS
and their applications are discussed, particularly for sensor employment.
In recent years, the utility grid system is more essential for the power
transmission and distribution system because it cannot produce harmful
gases or no discharge waste in the environment. PWM based phase
synchronous invert systems are generally utilised in the high efficiency
energy supply, long distance and higher power quality. The inverter output
voltage depends on the coupling transformer, input sources and invert
controllers. An inverter using a three leg IGBT has been designed for utility
grid and simulated by using MATLAB2014a. In this paper, both sides of
the LCL filters are used for removing the DC ripple current, reducing the
noise and synchronous the output phase between inverter and the utility
grid. The PWM controller has created pulse signal to control the inverter,
electronic switches and precisely synchronise with grid line frequency. In
this system, the input DC voltage 500V, switching frequency 1.65 kHz, grid
frequency 50Hz, 20 km feeder (resistance, inductance and capacitance per
unit length, which are 0.1153, 1.05e-3 and 11.33e-09 ohms/km) with 30MW
three phase load (active and inductive reactive power which are 30e6 W
and 2e6 var) and also a balanced utility grid load of star configuration (00,
1200, and 2400 degree) are considered in the design. On the other hand,
three phase transformer consists of three signal phase transformers, normal
power 100e3, magnetization resistance and inductance which are 500 pu and
416.67pu are considered in this design. The system conversion efficiency
is 99.94% and 99.96%, while the total THD are 0.06% on inverter side
and 0.04% on grid side.
Text localisation determines the location of the text in an image. This process
is performed prior to text recognition. Localising text on shop signage is
a challenging task since the images of the shop signage consist of complex
background, and the text occurs in various font types, sizes, and colours.
Two popular texture features that have been applied to localise text in
scene images are a histogram of oriented gradient (HOG) and speeded up
robust features (SURF). A comparative study is conducted in this paper
to determine which is better with support vector machine (SVM) classifier.
The performance of SVM is influenced by its kernel function and another
comparative study is conducted to identify the best kernel function. The
experiments have been conducted using primary data collected by the
authors. Results indicate that HOG with quadratic kernel function localises
text for shop signage better than SURF.