This paper presents various imputation methods for air quality data specifically in Malaysia. The main objective was to
select the best method of imputation and to compare whether there was any difference in the methods used between stations
in Peninsular Malaysia. Missing data for various cases are randomly simulated with 5, 10, 15, 20, 25 and 30% missing.
Six methods used in this paper were mean and median substitution, expectation-maximization (EM) method, singular
value decomposition (SVD), K-nearest neighbour (KNN) method and sequential K-nearest neighbour (SKNN) method. The
performance of the imputations is compared using the performance indicator: The correlation coefficient (R), the index
of agreement (d) and the mean absolute error (MAE). Based on the result obtained, it can be concluded that EM, KNN
and SKNN are the three best methods. The same result are obtained for all the eight monitoring station used in this study.
In this paper, we extended a repairable system model under general repair that is based on repair history, to incorporate covariates. We calculated the bias, standard error and RMSE of the parameter estimates of this model at different sample sizes using simulated data. We applied the model to a real demonstration data and tested for existence of time trend, repair and covariate effects. Following that we also conducted a coverage probability study on the Wald confidence interval estimates. Finally we conducted hypothesis testing for the parameters of the model.The results indicated that the estimation procedure is working well for the proposed model but the Wald interval should be applied with much caution.
Statistical modeling of extreme rainfall is essential since the results can often facilitate civil engineers and planners to estimate the ability of building structures to survive under the utmost extreme conditions. Data comprising of annual maximum series (AMS) of extreme rainfall in Alor Setar were fitted to Generalized Extreme Value (GEV) distribution using method of maximum likelihood (ML) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. The weakness of ML method in handling small sample is hoped to be tackled by means of Bayesian MCMC simulations in this study. In order to obtain the posterior densities, non-informative and independent priors were employed. Performances of parameter estimations were verified by conducting several goodness-of-fit tests. The results showed that Bayesian MCMC method was slightly better than ML method in estimating GEV parameters.
Missing values have always been a problem in analysis. Most exclude the missing values from the analyses which may lead to biased parameter estimates. Some imputations methods are considered in this paper in which simulation study is conducted to compare three methods of imputation namely mean substitution, hot deck and expectation maximization (EM) imputation. The EM imputation is found to be superior especially when the percentage of missing values is high as it constantly gives low RMSE as compared with other two methods. The EM imputation method is then applied to the PM10 concentrations data set for the southwest and northeast monsoons in Petaling Jaya and Seberang Perai, Malaysia which has missing values. Four types of distributions, namely the Weibull, lognormal, gamma and Gumbel distribution are considered to describe the PM10 concentrations. The Weibull distribution gives the best fit for the southwest monsoon data for Petaling Jaya. The lognormal distribution outperformed the others in describing the southwest monsoon in Seberang Perai. Meanwhile, for the northeast monsoon in both locations, gamma distribution is the best distribution to describe the data.
Multicomposition of Er3+ -Y11-3+ codoped tellurite oxide, Te02-ZnO-PbO-Ti02-Na20 glass has been investigated. A detailed spectroscopic study of the Judd-Ofelt analysis has been performed from the measured absorption spectrum in order to obtain the intensity parameters S2, (t=2, 4, 6). The calculated S2, values were then utilized in the determination of transition probabilities, radiative lifetimes and branching ratios of the Er3+ transitions between the J(upper)-J'(lower) manifolds. Both visible upconversion and near-infrared spectra were characterized under the 980 nm laser diode excitation at room temperature.
This research proposes a point forecasting method into Markov switching autoregressive model. In case of two regimes, we proved the probability that h periods later process will be in regime 1 or 2 is given by steady-state probabilities. Then, using the value of h-step-ahead forecast data at time t in each regime and using steady-state probabilities, we present an h-step-ahead point forecast of data. An empirical application of this forecasting technique for U.S. Dollar/ Euro exchange rate showed that Markov switching autoregressive model achieved superior forecasts relative to the random walk with drift. The results of out-of-sample forecast indicate that the fluctuations of U.S. Dollar/ Euro exchange rate from May 2011 to May 2013 will be rising.
Integrating an exit choice model into a microscopic crowd dynamics model is an essential approach for obtaining more
efficient evacuation model. We describe various aspects of decision-making capability of an existing rule-based exit
choice model for evacuation processes. In simulations, however, the simulated evacuees clogging at exits have behaved
non-intelligently, namely they do not give up their exits for better ones for safer egress. We refine the model to endow
the individuals with the ability to leave their exits due to dynamic changes by modifying the model of their excitement
resulted from the source of panic. This facilitates the approximately equal crowd size at exits for being until the end
of the evacuation process, and thereby the model accomplishes more optimal evacuation. For further intelligence, we
introduce the prediction factor that enables higher probability of equally distributing evacuees at exits. A simulation to
validate the contribution is performed, and the results are analyzed and compared with the original model.
The stability of the limestone cliff at Gunung Kandu, Gopeng, Perak, Malaysia was assessed based on the Slope Mass
Rating (SMR) system on 53 cross sections of the Gunung Kandu hill slopes. The slopes of Gunung Kandu were identified
as class I (very good) to IV (poor). The kinematic analysis showed that 12 out of 53 hill slopes of Gunung Kandu were
identified as having potential wedge, planar and toppling failures. The assessment showed that the stability of the western
flanks can be classified as stable to unstable with the probability of failure from 0.2 to 0.6. The stability of the eastern and
southern flanks range from very stable to partially stable with the probability of failure from 0.0 to 0.4. While the stability
of northern flanks are from very stable to stable with the probability of failure of 0.0 - 0.2. This systematic approach
offers a practical method especially for large area of rock slope stability assessment and the results from probability of
failure values will help engineers to design adequate mitigation measures.
Safety is vital in any industry, including the offshore sector, which is classified as a major hazard industry. Health, Safety and the Environment (HSE) identified that the probability of accidents is high while working on the offshore sectors where it will exposed workers to many hazardous work activities. The appropriate measures to prevent accident in this sectors must be laid out clearly. This paper is to identify the effectiveness of safety awareness campaign and the continuity of the awareness among the workers to prevent injuries at offshore. To achieve this, we have identified the level of awareness and propose a guideline on areas of improvement. Prior of embarking to offshore, staff were exposed to safety awareness program for four weeks. After the program, we started with the pretest to all staff. They were posted offshore for 6 weeks. Within the period, the performance awareness of each staff is monitored through observation and interview. During the final week, the posttest questionnaire were administered to all staff. Two instruments were used for the quantitative data collection, which are Unsafe Act Unsafe Condition (UAUC) card; and Behavior Observation Tool (BOT) card. Questionnaire data were analyzed quantitatively. Paired-sample t-test was used for analyzing pre and post result. The results show that the mean was increased. Recent studies on the safety briefing highlighted several significant changes in terms of employee understanding toward safety. Safety awareness training has been introduced in the new safety briefing prior to offshore mobilization.
One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable to legal users. This can be accomplished mainly by directing many machines to send a very large number of packets toward the specified machine to consume its resources and stop it from working. We implemented a method using Java based on entropy and sequential probabilities ratio test (ESPRT) methods to identify malicious flows and their switch interfaces that aid them in passing through. Entropy (E) is the first technique, and the sequential probabilities ratio test (SPRT) is the second technique. The entropy method alone compares its results with a certain threshold in order to make a decision. The accuracy and F-scores for entropy results thus changed when the threshold values changed. Using both entropy and SPRT removed the uncertainty associated with the entropy threshold. The false positive rate was also reduced when combining both techniques. Entropy-based detection methods divide incoming traffic into groups of traffic that have the same size. The size of these groups is determined by a parameter called window size. The Defense Advanced Research Projects Agency (DARPA) 1998, DARPA2000, and Canadian Institute for Cybersecurity (CIC-DDoS2019) databases were used to evaluate the implementation of this method. The metric of a confusion matrix was used to compare the ESPRT results with the results of other methods. The accuracy and f-scores for the DARPA 1998 dataset were 0.995 and 0.997, respectively, for the ESPRT method when the window size was set at 50 and 75 packets. The detection rate of ESPRT for the same dataset was 0.995 when the window size was set to 10 packets. The average accuracy for the DARPA 2000 dataset for ESPRT was 0.905, and the detection rate was 0.929. Finally, ESPRT was scalable to a multiple domain topology application.
Pre-stressing is a concept used in many engineering structures. In this study prestressing in the form of axial compression stress is proposed in the blade structure of H-Darrieus wind turbine. The study draws a structural comparison between reference and prestressed configurations of turbine rotor with respect to their dynamic vibrational response. Rotordynamics calculations provided by ANSYS Mechanical is used to investigate the effects of turbine rotation on the dynamic response of the system. Rotation speed ranging between 0 to 150 rad/s was examined to cover the whole operating range of commercial instances. The modal analysis ends up with first six mode shapes of both rotor configurations. As a result, the displacement of the proposed configurations reduced effectively. Apparent variations in Campbell diagrams of both cases indicate that prestressed configuration has its resonant frequencies far away from turbine operation speeds and thus remarkably higher safety factor against whirling and probable following failures.
Interpretation of comparative Life Cycle Assessment (LCA) results can be challenging in the presence of uncertainty. To aid in interpreting such results under the goal of any comparative LCA, we aim to provide guidance to practitioners by gaining insights into uncertainty-statistics methods (USMs). We review five USMs-discernibility analysis, impact category relevance, overlap area of probability distributions, null hypothesis significance testing (NHST), and modified NHST-and provide a common notation, terminology, and calculation platform. We further cross-compare all USMs by applying them to a case study on electric cars. USMs belong to a confirmatory or an exploratory statistics' branch, each serving different purposes to practitioners. Results highlight that common uncertainties and the magnitude of differences per impact are key in offering reliable insights. Common uncertainties are particularly important as disregarding them can lead to incorrect recommendations. On the basis of these considerations, we recommend the modified NHST as a confirmatory USM. We also recommend discernibility analysis as an exploratory USM along with recommendations for its improvement, as it disregards the magnitude of the differences. While further research is necessary to support our conclusions, the results and supporting material provided can help LCA practitioners in delivering a more robust basis for decision-making.
This paper offers a technique to construct a prediction interval for the future value of the last variable in the vector r of m variables when the number of observed values of r is small. Denoting r(t) as the time-t value of r, we model the time-(t+1) value of the m-th variable to be dependent on the present and l-1 previous values r(t), r(t-1), …, r(t-l+1) via a conditional distribution which is derived from an (ml+1)-dimensional power-normal distribution. The 100(α / 2)% and 100(1−α / 2)% points of the conditional distribution may then be used to form a prediction interval for the future value of the m-th variable. A method is introduced to estimate the above (ml+1)-dimensional power-normal distribution such that the coverage probability of the resulting prediction interval is nearer to the target value 1- α .
Score-based structure learning algorithm is commonly used in learning the Bayesian Network. Other than searching strategy, scoring functions play a vital role in these algorithms. Many studies proposed various types of scoring functions with different characteristics. In this study, we compare the performances of five scoring functions: Bayesian Dirichlet equivalent-likelihood (BDe) score (equivalent sample size, ESS of 4 and 10), Akaike Information Criterion (AIC) score, Bayesian Information Criterion (BIC) score and K2 score. Instead of just comparing networks with different scores, we included different learning algorithms to study the relationship between score functions and greedy search learning algorithms. Structural hamming distance is used to measure the difference between networks obtained and the true network. The results are divided into two sections where the first section studies the differences between data with different number of variables and the second section studies the differences between data with different sample sizes. In general, the BIC score performs well and consistently for most data while the BDe score with an equivalent sample size of 4 performs better for data with bigger sample sizes.
Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes are not easily or explicitly detectable. Such alterations have been recently used to spread false news or disinformation. This study aims to identify Deepfaked videos and images and alert viewers to the possible falsity of the information. The current work presented a novel means of revealing fake face videos by cascading the convolution network with recurrent neural networks and fully connected network (FCN) models. The system detection approach utilizes the eye-blinking state in temporal video frames. Notwithstanding, it is deemed challenging to precisely depict (i) artificiality in fake videos and (ii) spatial information within the individual frame through this physiological signal. Spatial features were extracted using the VGG16 network and trained with the ImageNet dataset. The temporal features were then extracted in every 20 sequences through the LSTM network. On another note, the pre-processed eye-blinking state served as a probability to generate a novel BPD dataset. This newly-acquired dataset was fed to three models for training purposes with each entailing four, three, and six hidden layers, respectively. Every model constitutes a unique architecture and specific dropout value. Resultantly, the model optimally and accurately identified tampered videos within the dataset. The study model was assessed using the current BPD dataset based on one of the most complex datasets (FaceForensic++) with 90.8% accuracy. Such precision was successfully maintained in datasets that were not used in the training process. The training process was also accelerated by lowering the computation prerequisites.
Extensive hydrological analysis is carried out to estimate floods for the Batu Dam, a hydropower dam located in the urban area upstream of Kuala Lumpur, Malaysia. The study demonstrates the operational state and reliability of the dam structure based on hydrologic assessment of the dam. The surrounding area is affected by heavy rainfall and climate change every year, which increases the probability of flooding and threatens a dense population downstream of the dam. This study evaluates the adequacy of dam spillways by considering the latest Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) values of the concerned dams. In this study, the PMP estimations are applied using comparison of both statistical method by Hershfield and National Hydraulic Research Institute of Malaysia (NAHRIM) Envelope Curve as input for PMF establishments. Since the PMF is derived from the PMP values, the highest design flood standard can be applied to any dam, ensuring inflow into the reservoirs and limiting the risk of dam structural failure. Hydrologic modeling using HEC-HMS provides PMF values for the Batu dam. Based on the results, Batu Dam is found to have 200.6 m3/s spillway discharge capacities. Under PMF conditions, the Batu dam will not face overtopping since the peak outflow of the reservoir level is still below the crest level of the dam.
The probabilistic hesitant elements (PHFEs) are a beneficial augmentation to the hesitant fuzzy element (HFE), which is intended to give decision-makers more flexibility in expressing their biases while using hesitant fuzzy information. To extrapolate a more accurate interpretation of the decision documentation, it is sufficient to standardize the organization of the elements in PHFEs without introducing fictional elements. Several processes for unifying and arranging components in PHFEs have been proposed so far, but most of them result in various disadvantages that are critically explored in this paper. The primary objective of this research is to recommend a PHFE unification procedure that avoids the deficiencies of operational practices while maintaining the inherent properties of PHFE probabilities. The prevailing study advances the hypothesis of permutation on PHFEs by suggesting a new sort of PHFS division and subtraction compared with the existing unification procedure. Eventually, the proposed PHFE-unification process will be used in this study, an innovative PHFEs based on the Weighted Aggregated Sum Product Assessment Method-Analytic Hierarchy Process (WASPAS-AHP) perspective for selecting flexible packaging bags after the prohibition on single-use plastics. As a result, we have included the PHFEs-WASPAS in our selection of the most effective fuzzy environment for bio-plastic bags. The ranking results for the suggested PHFEs-MCDM techniques surpassed the existing AHP methods in the research study by providing the best solution. Our solutions offer the best bio-plastic bag alternative strategy for mitigating environmental impacts.
The banking industry necessitates implementing an early warning system to effectively identify the factors that impact bank managers and enable them to make informed decisions, thereby mitigating systemic risk. Identifying factors that influence banks in times of stability and crisis is crucial, as it ultimately contributes to developing an improved early warning system. This study undertakes a comparative analysis of the stability of Indonesian Islamic and conventional banking across distinct economic regimes-crisis and stability. We analyze monthly banking data from December 2007 to November 2022 using the Markov Switching Dynamic Regression technique. The study focuses on conducting a comparative analysis between Islamic banks, represented by Islamic Commercial Bank (ICB) and Islamic Rural Bank (IRB), and conventional banks, represented by the Conventional Commercial Bank (CCB) and Conventional Rural Bank (CRB). The findings reveal that both Islamic and conventional banks exhibit a higher probability of being in a stable regime than a crisis regime. Notably, Islamic banks demonstrate a greater propensity to remain in a stable regime than their conventional counterparts. However, in a crisis regime, the likelihood of recovery for Sharia-compliant institutions is lower than for conventional banks. Furthermore, our analysis indicates that larger banks exhibit higher stability than their smaller counterparts regarding assets and size. This study pioneers a comprehensive comparison of the Z-score, employed as a proxy for stability, between two distinct classifications of Indonesian banks: Sharia (ICB and IRB) and conventional (CCB and CRB). The result is expected to improve our awareness of the elements that affect the stability of Islamic and conventional banking in Indonesia, leading to a deeper comprehension of their dynamics.
BACKGROUND: Optimisation of average glandular dose (AGD) for two-dimensional (2D) mammography is important, as imaging using ionizing radiation has the probability to induce cancer resulting from stochastic effects. This study aims to observe the effects of kVp, anode/filter material, and exposure mode on the dose and image quality of 2D mammography.
METHODS: This experimental study was conducted using full-field digital mammography. The entrance surface air kerma was determined using thermoluminescent dosimeter (TLD) 100H and ionization chamber (IC) on three types of Computerized Imaging Reference System (CIRS) phantom with 50/50, 30/70, and 20/80 breast glandularity, respectively, in the auto-time mode and auto-filter mode. The Euref protocol was used to calculate the AGD while the image quality was evaluated using contrast-to-noise ratio (CNR), figure of merit (FOM), and image quality figure (IQF).
RESULTS: It is shown that AGD values in the auto-time mode did not decrease significantly with the increasing tube voltage of the silver filter (r = -0.187, P > 0.05) and rhodium filter (r = -0.131, P > 0.05) for all the phantoms. The general linear model showed that AGD for all phantoms had a significant effect between different exposure factors [F (6,12.3) = 4.48 and mode of exposure F (1,86) = 4.17, P < 0.05, respectively] but there is no significant difference between the different anode/filter combination [F (1,4) = 0.571].
CONCLUSION: In summary, the 28, 29, and 31 kVp are the optimum kVp for 50%, 30%, and 20% breast glandularity, respectively. Besides the auto-filter mode is suitable for 50%, 30%, and 20% breast glandularity because it is automatic, faster, and may avoid error done by the operator.
KEYWORDS: CDMAM; digital mammography; radiation dose
Mesenchymal chondrosarcoma is a rare disease with poor prognosis. Treatment including wide or radical excision is very important. Radiotherapy and chemotherapy are additional treatment options, but no conclusive results for their efficacy have been shown until date. Imaging modalities can give important clues for diagnosis and management planning. Angioembolization before surgery could be useful as prophylaxis to control intraoperative bleeding, increasing the likelihood of complete resection.