Displaying publications 1 - 20 of 1459 in total

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  1. Loh BCS, Then PHH
    Mhealth, 2017;3:45.
    PMID: 29184897 DOI: 10.21037/mhealth.2017.09.01
    Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.
    Matched MeSH terms: Algorithms
  2. Mehbodniya A, Moghavvemi M, Narayanan V, Muthusamy KA, Hamdi M, Waran V
    World Neurosurg, 2020 Feb;134:e379-e386.
    PMID: 31639505 DOI: 10.1016/j.wneu.2019.10.080
    OBJECTIVES: The evaluation of sources of error when preparing, printing, and using 3-dimensional (3D) printed head models for training purposes.

    METHODS: Two 3D printed models were designed and fabricated using actual patient imaging data with reference marker points embedded artificially within these models that were then registered to a surgical navigation system using 3 different methods. The first method uses a conventional manual registration, using the actual patient's imaging data. The second method is done by directly scanning the created model using intraoperative computed tomography followed by registering the model to a new imaging dataset manually. The third is similar to the second method of scanning the model but eventually uses an automatic registration technique. The errors for each experiment were then calculated based on the distance of the surgical navigation probe from the respective positions of the embedded marker points.

    RESULTS: Errors were found in the preparation and printing techniques, largely depending on the orientation of the printed segment and postprocessing, but these were relatively small. Larger errors were noted based on a couple of variables: if the models were registered using the original patient imaging data as opposed to using the imaging data from directly scanning the model (1.28 mm vs. 1.082 mm), and the accuracy was best using the automated registration techniques (0.74 mm).

    CONCLUSION: Spatial accuracy errors occur consistently in every 3D fabricated model. These errors are derived from the fabrication process, the image registration process, and the surgical process of registration.

    Matched MeSH terms: Algorithms
  3. Hassan MZ, Rathnayaka MM, Deen KI
    World J Surg, 2010 Jul;34(7):1641-7.
    PMID: 20180122 DOI: 10.1007/s00268-010-0489-1
    We undertook a prospective longitudinal study of patients with end-stage fecal incontinence who were undergoing transposition of the gracilis muscle as a neo-anal sphincter with external low-frequency electrical stimulation of the nerve to the gracilis combined with biofeedback.
    Matched MeSH terms: Algorithms
  4. Chun TS, Malek MA, Ismail AR
    Water Sci Technol, 2015;71(4):524-8.
    PMID: 25746643 DOI: 10.2166/wst.2014.451
    The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
    Matched MeSH terms: Algorithms*
  5. Noor Rodi NS, Malek MA, Ismail AR, Ting SC, Tang CW
    Water Sci Technol, 2014;70(10):1641-7.
    PMID: 25429452 DOI: 10.2166/wst.2014.420
    This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.
    Matched MeSH terms: Algorithms*
  6. Bong CH, Lau TL, Ab Ghani A
    Water Sci Technol, 2013;68(11):2397-406.
    PMID: 24334888 DOI: 10.2166/wst.2013.498
    This paper highlights a preliminary study on the potential of a tipping flush gate to be used in an open storm drain to remove sediment. The investigation was carried out by using a plasboard model of the tipping flush gate installed in a rectangular flume. A steady flow experiment was carried out to determine the discharge coefficients and also the outflow relationship of the tipping flush gate. The velocity produced by the gate at various distances downstream of the gate during flushing operation was measured using a flowmeter and the velocity at all the points was higher than the recommended self-cleansing design available in the literature. A preliminary experiment on the efficiency of flushing was conducted using uniform sediment with d50 sizes of 0.81, 1.53 and 4.78 mm. Results generally showed that the number of flushes required to totally remove the sediment from the initial position by a distance of 1 m increased by an average of 1.50 times as the sediment deposit bed thickness doubled. An equation relating the number of flushes required to totally remove the sediment bed for 1 m with the sediment bed deposit thickness was also developed for the current study.
    Matched MeSH terms: Algorithms
  7. Nor-Anuar A, Ujang Z, van Loosdrecht MC, de Kreuk MK, Olsson G
    Water Sci Technol, 2012;65(2):309-16.
    PMID: 22233910 DOI: 10.2166/wst.2012.837
    Aerobic granular sludge has a number of advantages over conventional activated sludge flocs, such as cohesive and strong matrix, fast settling characteristic, high biomass retention and ability to withstand high organic loadings, all aspects leading towards a compact reactor system. Still there are very few studies on the strength of aerobic granules. A procedure that has been used previously for anaerobic granular sludge strength analysis was adapted and used in this study. A new coefficient was introduced, called a stability coefficient (S), to quantify the strength of the aerobic granules. Indicators were also developed based on the strength analysis results, in order to categorize aerobic granules into three levels of strength, i.e. very strong (very stable), strong (stable) and not strong (not stable). The results indicated that aerobic granules grown on acetate were stronger (high density: >150 g T SSL(-1) and low S value: 5%) than granules developed on sewage as influent. A lower value of S indicates a higher stability of the granules.
    Matched MeSH terms: Algorithms
  8. Bonakdari H, Ebtehaj I, Akhbari A
    Water Sci Technol, 2017 Jun;75(12):2791-2799.
    PMID: 28659519 DOI: 10.2166/wst.2017.158
    Electrocoagulation (EC) is employed to investigate the energy consumption (EnC) of synthetic wastewater. In order to find the best process conditions, the influence of various parameters including initial pH, initial dye concentration, applied voltage, initial electrolyte concentration, and treatment time are investigated in this study. EnC is considered the main criterion of process evaluation in investigating the effect of the independent variables on the EC process and determining the optimum condition. Evolutionary polynomial regression is combined with a multi-objective genetic algorithm (EPR-MOGA) to present a new, simple and accurate equation for estimating EnC to overcome existing method weaknesses. To survey the influence of the effective variables, six different input combinations are considered. According to the results, EPR-MOGA Model 1 is the most accurate compared to other models, as it has the lowest error indices in predicting EnC (MARE = 0.35, RMSE = 2.33, SI = 0.23 and R2 = 0.98). A comparison of EPR-MOGA with reduced quadratic multiple regression methods in terms of feasibility confirms that EPR-MOGA is an effective alternative method. Moreover, the partial derivative sensitivity analysis method is employed to analyze the EnC variation trend according to input variables.
    Matched MeSH terms: Algorithms
  9. Fiyadh SS, AlSaadi MA, AlOmar MK, Fayaed SS, Hama AR, Bee S, et al.
    Water Sci Technol, 2017 Nov;76(9-10):2413-2426.
    PMID: 29144299 DOI: 10.2166/wst.2017.393
    The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.
    Matched MeSH terms: Algorithms
  10. Islam MS, Hannan MA, Basri H, Hussain A, Arebey M
    Waste Manag, 2014 Feb;34(2):281-90.
    PMID: 24238802 DOI: 10.1016/j.wasman.2013.10.030
    The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.
    Matched MeSH terms: Algorithms*
  11. Hannan MA, Arebey M, Begum RA, Basri H
    Waste Manag, 2011 Dec;31(12):2406-13.
    PMID: 21871788 DOI: 10.1016/j.wasman.2011.07.022
    This paper deals with a system of integration of Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system. RFID, GPS, GPRS and GIS along with camera technologies have been integrated and developed the bin and truck intelligent monitoring system. A new kind of integrated theoretical framework, hardware architecture and interface algorithm has been introduced between the technologies for the successful implementation of the proposed system. In this system, bin and truck database have been developed such a way that the information of bin and truck ID, date and time of waste collection, bin status, amount of waste and bin and truck GPS coordinates etc. are complied and stored for monitoring and management activities. The results showed that the real-time image processing, histogram analysis, waste estimation and other bin information have been displayed in the GUI of the monitoring system. The real-time test and experimental results showed that the performance of the developed system was stable and satisfied the monitoring system with high practicability and validity.
    Matched MeSH terms: Algorithms*
  12. Akhtar M, Hannan MA, Begum RA, Basri H, Scavino E
    Waste Manag, 2017 Mar;61:117-128.
    PMID: 28153405 DOI: 10.1016/j.wasman.2017.01.022
    Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.
    Matched MeSH terms: Algorithms*
  13. Hannan MA, Akhtar M, Begum RA, Basri H, Hussain A, Scavino E
    Waste Manag, 2018 Jan;71:31-41.
    PMID: 29079284 DOI: 10.1016/j.wasman.2017.10.019
    Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts.
    Matched MeSH terms: Algorithms*
  14. Henry EB, Barry LE, Hobbins AP, McClure NS, O'Neill C
    Value Health, 2020 07;23(7):936-944.
    PMID: 32762996 DOI: 10.1016/j.jval.2020.03.003
    OBJECTIVES: To estimate and compare the minimally important difference (MID) in index score of country-specific EQ-5D-5L scoring algorithms developed using EuroQol Valuation Technology protocol version 2, including algorithms from Germany, Indonesia, Ireland, Malaysia, Poland, Portugal, Taiwan, and the United States.

    METHODS: A simulation-based approach contingent on all single-level transitions defined by the EQ-5D-5L descriptive system was used to estimate the MID for each algorithm.

    RESULTS: The resulting mean (and standard deviation) instrument-defined MID estimates were Germany, 0.083 (0.022); Indonesia, 0.093 (0.012); Ireland, 0.098 (0.023); Malaysia, 0.072 (0.010); Poland, 0.080 (0.030); Portugal, 0.080 (0.018); Taiwan, 0.101 (0.010); and the United States, 0.078 (0.014).

    CONCLUSIONS: These population preference-based MID estimates and accompanying evidence of how such values vary as a function of baseline index score can be used to aid interpretation of index score change. The marked consistency in the relationship between the calculated MID estimate and the range of the EQ-5D-5L index score, represented by a ratio of 1:20, might substantiate a rule of thumb allowing for MID approximation in EQ-5D-5L index score warranting further investigation.

    Matched MeSH terms: Algorithms
  15. Alhabshi SM, Rahmat K, Abdul Halim N, Aziz S, Radhika S, Gan GC, et al.
    Ultrasound Med Biol, 2013 Apr;39(4):568-78.
    PMID: 23384468 DOI: 10.1016/j.ultrasmedbio.2012.10.016
    The purpose of this study was to evaluate the diagnostic value of qualitative and semi-quantitative assessment of ultrasound elastography in differentiating between benign and malignant breast lesions. This prospective study was conducted in two tertiary medical centers. Consecutive B-mode ultrasound and real-time elastographic images were obtained for 67 malignant and 101 benign breast lesions in 168 women. Four experienced radiologists analyzed B-mode ultrasound alone and B-mode ultrasound combined with elastography independently. Conventional ultrasound findings were classified according to the American College of Radiology Breast Imaging Reporting and Data System classification. The elastographic assessment was based on qualitative and semi-quantitative parameters (i.e., strain pattern, width ratio, strain ratio). The sensitivity and specificity of combined elastography and conventional ultrasound were significantly higher than that of conventional ultrasound alone. The sensitivity, specificity, positive predictive value and negative predictive value was 97%, 61.4%, 62.5% and 96.8%, respectively, for conventional ultrasound and 100%, 93%, 99% and 90%, respectively, for combined technique. The semi-quantitative assessment with strain ratio and width ratio in elastography were the most useful parameters in differentiating between benign and malignant breast lesions. Cut-off point values for width ratio of more than 1.1 and strain ratio of more than 5.6 showed a high predictive value of malignancy with specificities of 84% and 76%, respectively (p 
    Matched MeSH terms: Algorithms*
  16. Tiong TJ, Price GJ, Kanagasingam S
    Ultrason Sonochem, 2014 Sep;21(5):1858-65.
    PMID: 24735986 DOI: 10.1016/j.ultsonch.2014.03.024
    One of the uses of ultrasound in dentistry is in the field of endodontics (i.e. root canal treatment) in order to enhance cleaning efficiency during the treatment. The acoustic pressures generated by the oscillation of files in narrow channels has been calculated using the COMSOL simulation package. Acoustic pressures in excess of the cavitation threshold can be generated and higher values were found in narrower channels. This parallels experimental observations of sonochemiluminescence. The effect of varying the channel width and length and the dimensions and shape of the file are reported. As well as explaining experimental observations, the work provides a basis for the further development and optimisation of the design of endosonic files.
    Matched MeSH terms: Algorithms
  17. Fayyazi E, Ghobadian B, Najafi G, Hosseinzadeh B, Mamat R, Hosseinzadeh J
    Ultrason Sonochem, 2015 Sep;26:312-20.
    PMID: 25870003 DOI: 10.1016/j.ultsonch.2015.03.007
    Biodiesel is a green (clean), renewable energy source and is an alternative for diesel fuel. Biodiesel can be produced from vegetable oil, animal fat and waste cooking oil or fat. Fats and oils react with alcohol to produce methyl ester, which is generally known as biodiesel. Because vegetable oil and animal fat wastes are cheaper, the tendency to produce biodiesel from these materials is increasing. In this research, the effect of some parameters such as the alcohol-to-oil molar ratio (4:1, 6:1, 8:1), the catalyst concentration (0.75%, 1% and 1.25% w/w) and the time for the transesterification reaction using ultrasonication on the rate of the fatty acids-to-methyl ester (biodiesel) conversion percentage have been studied (3, 6 and 9 min). In biodiesel production from chicken fat, when increasing the catalyst concentration up to 1%, the oil-to-biodiesel conversion percentage was first increased and then decreased. Upon increasing the molar ratio from 4:1 to 6:1 and then to 8:1, the oil-to-biodiesel conversion percentage increased by 21.9% and then 22.8%, respectively. The optimal point is determined by response surface methodology (RSM) and genetic algorithms (GAs). The biodiesel production from chicken fat by ultrasonic waves with a 1% w/w catalyst percentage, 7:1 alcohol-to-oil molar ratio and 9 min reaction time was equal to 94.8%. For biodiesel that was produced by ultrasonic waves under a similar conversion percentage condition compared to the conventional method, the reaction time was decreased by approximately 87.5%. The time reduction for the ultrasonic method compared to the conventional method makes the ultrasonic method superior.
    Matched MeSH terms: Algorithms*
  18. Hamidi H, Mohammadian E, Junin R, Rafati R, Manan M, Azdarpour A, et al.
    Ultrasonics, 2014 Feb;54(2):655-62.
    PMID: 24075416 DOI: 10.1016/j.ultras.2013.09.006
    Theoretically, Ultrasound method is an economical and environmentally friendly or "green" technology, which has been of interest for more than six decades for the purpose of enhancement of oil/heavy-oil production. However, in spite of many studies, questions about the effective mechanisms causing increase in oil recovery still existed. In addition, the majority of the mechanisms mentioned in the previous studies are theoretical or speculative. One of the changes that could be recognized in the fluid properties is viscosity reduction due to radiation of ultrasound waves. In this study, a technique was developed to investigate directly the effect of ultrasonic waves (different frequencies of 25, 40, 68 kHz and powers of 100, 250, 500 W) on viscosity changes of three types of oil (Paraffin oil, Synthetic oil, and Kerosene) and a Brine sample. The viscosity calculations in the smooth capillary tube were based on the mathematical models developed from the Poiseuille's equation. The experiments were carried out for uncontrolled and controlled temperature conditions. It was observed that the viscosity of all the liquids was decreased under ultrasound in all the experiments. This reduction was more significant for uncontrolled temperature condition cases. However, the reduction in viscosity under ultrasound was higher for lighter liquids compare to heavier ones. Pressure difference was diminished by decreasing in the fluid viscosity in all the cases which increases fluid flow ability, which in turn aids to higher oil recovery in enhanced oil recovery (EOR) operations. Higher ultrasound power showed higher liquid viscosity reduction in all the cases. Higher ultrasound frequency revealed higher and lower viscosity reduction for uncontrolled and controlled temperature condition experiments, respectively. In other words, the reduction in viscosity was inversely proportional to increasing the frequency in temperature controlled experiments. It was concluded that cavitation, heat generation, and viscosity reduction are three of the promising mechanisms causing increase in oil recovery under ultrasound.
    Matched MeSH terms: Algorithms*
  19. CHUA KAH WAI, LOY KAK CHOON, RUWAIDIAH IDRIS
    MyJurnal
    Ordinary Differential Equations (ODEs) are usually used in numerous fields especially in solving the modelling problem. Numerical methods are one of the vital mathematical tools to solve the ODEs that appear in various modelling problems by determining the approximation solution close to the in exact solution if it exists. Runge-Kutta methods (RK) are the numerical methods used to integrate the ODEs by applying multistage methods at the midpoint of an interval which can efficiently produce a more accurate result or small magnitude of error. We proposed Runge-Kutta methods (RK) to solve the 1st_ order nonlinear stiff ODEs. The RK methods used in this research are known as the RK-2, RK-4, and RK-5 methods. We proved the existence and uniqueness of the ODEs before we solved it numerically. We also proved the absolute-stability of the RK methods to determine the overall stability of these methods. We found two suitable test cases which are the standard test problem and manufactured solution. We proved that by combining the adaptive step size with RK methods can result in more efficient computation. We implemented the 2nd_, 4th_ and 5th_ order of RK methods with step size adaptively algorithm to solve the test problem and manufactured solution via Octave programming language. The resulting numerical error and the stability of each method can be studied. We compared our results using several error plots versus the Central Processing Unit (CPU) time required to compute a given nonlinear 1st_ order stiff ODE problem. In a conclusion, RK methods which combine with the adaptive step size can result in more efficient computation and accuracy compare with the fixed step size RK methods.
    Matched MeSH terms: Algorithms
  20. MOHD ASLAN MOLENG, AHMAD FAIZAL AHMAD FUAD, MOHD HAFIZI SAID
    MyJurnal
    Trawling is a method of catching fish in a large volume where fish net is pulled through water using one or two boats. Bottom trawling is where the nets are pulled over or close to seabed and can affect the subsea pipeline if found along the route. Therefore, the objective of this study was to determine the impact of pull-over to selected subsea pipelines in Sabah and Labuan waters. This study involved four oil and gas pipelines in Sabah and Labuan waters from the oil fields to shore terminals. The research started with obtaining data of the pipelines and specification of trawl gear in Sabah. Fishing trawler traffic data along the pipelines route was determined by AIS system and site observation to determine the density of the trawlers. Trawl gear pull-over load was calculated using DNV algorithm and the inputs were trawl gear specification ^and fishing trawl speed. The severity was based on pull-over load calculated and pipeline yield stress. Then frequency was based on AIS data and density of fishing trawl per area. Based on the comparison between trawl pull-over load and yield strength/stress, the effect of trawl board pull-over is considered as minor, which is the lowest in the severity index.
    Matched MeSH terms: Algorithms
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