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Fulltext THE EFFECT OF TRAWL BOARD PULL-OVER LOAD ON SUBSEA PIPELINE OF OFFSHORE SABAH

MOHD ASLAN MOLENG, AHMAD FAIZAL AHMAD FUAD, MOHD HAFIZI SAID

UMT Journal of Undergraduate Research, 2020;2(4):9-16.
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
Fulltext BOOSTING STUDENTS’ PERFORMANCE WITH THEAID OF SOCIAL NETWORK ANALYSIS

GOBITHAASAN RUDRUSAMY, NURUL SYAHEERA DIN, LINGESWARAN RAMACHANDRAN, ROSLAN HASNI

UMT Journal of Undergraduate Research, 2019;1(3):28-35.
MyJurnal

There are various teaching methods developed in order to attainsuccessful delivery of a subject without prior knowledge of the interaction among the students in a class. Social network analysis (SNA) can be used to identify individual, intermediate and group measures of interaction in a classroom. The idea is on identifying ways to boost the students’ performance by means of lecturer’s intervention based on their interaction. The case study was conducted involvingthird year batchthat consistedof 76 female and 24 male students. A friendship network was drawn based on the information obtained at the end of semester 5 and it wasinvestigated based on two metrics–centralitymeasures and Girvan-Newman algorithm. At the end of semester 5, grades were added asthe attributes of the network.12 clusters were found in this batch and a distinct pattern was identified between performing and poor achieving students. At the beginning of the 6th semester, the studentsweregiven the option to choose between 2 groups. Group 1 was unperturbed without any lecturer’s intervention whereas the performing students’ clusters in Group 1 were preserved but the students in poor performing clusters were distributed among performing clusters. The students were then asked to carry out assignments/quizzesin their respective groups. The final grades indicatedthat the performance of the students of Group 1 wasmuch superior and there wasclear evidence that those poor performing students in the 5th semester performed much better in semester 6. This shows that by understanding the students’ interaction and incorporatiniginstructor’s minimal intervention, the performance of the students can be improved by creating a social contagion effect through group assignment clustering.

Matched MeSH terms: Algorithms
Fulltext A baseline measure of tree and gastropod biodiversity in replanted and natural mangrove stands in malaysia: langkawi island and sungai merbok

Hookham B, Shau-Hwai AT, Dayrat B, Hintz W

Trop Life Sci Res, 2014 Aug;25(1):1-12.
PMID: 25210584 MyJurnal

THE DIVERSITIES OF MANGROVE TREES AND OF THEIR ASSOCIATED GASTROPODS WERE ASSESSED FOR TWO MANGROVE REGIONS ON THE WEST COAST OF PENINSULAR MALAYSIA: Langkawi Island and Sungai Merbok. The mangrove area sampled on Langkawi Island was recently logged and replanted, whereas the area sampled in Sungai Merbok was part of a protected nature reserve. Mangrove and gastropod diversity were assessed in four 50 m(2) (10 × 5 m) sites per region. The species richness (S), Shannon Index (H') and Evenness Index (J') were calculated for each site, and the mean S, H' and J' values were calculated for each region. We report low tree and gastropod S, H' and J' values in all sites from both regions. For Langkawi Island, the mean S, H' and J' values for mangrove trees were S = 2.00±0, H' = 0.44±0.17 and J' = 0.44±0.17; the mean S, H' and J' values for gastropods were S = 4.00±1.63, H' = 0.96±0.41 and J' = 0.49±0.06. In Sungai Merbok, the mean S, H' and J' values for mangrove trees were S = 1.33±0.58, H' = 0.22±0.39 and J' = 0.22 ±0.39; the mean S, H' and J' values for gastropods were S = 4.75±2.22, H' = 1.23±0.63 and J' = 0.55±0.12. This study emphasises the need for baseline biodiversity measures to be established in mangrove ecosystems to track the impacts of anthropogenic disturbances and to inform management and restoration efforts.

Matched MeSH terms: Algorithms
Fulltext The distribution of anacardiaceae in teluk bahang forest reserve, pulau pinang

Juperi S, Zakaria R, Mansor A

Trop Life Sci Res, 2012 May;23(1):35-44.
PMID: 24575224 MyJurnal

To investigate the distribution of Anacardiaceae in Teluk Bahang Permanent Forest Reserve (TBPFR) in Pulau Pinang, all trees with a diameter at breast high (DBH) ≥ 5 cm were enumerated in a study site constituting 0.4 ha of the reserve. Seventy five individuals of Anacardiaceae (14% of all trees) are recorded. These individuals represent 4 genera and 5 species, namely, Mangifera pentandra, Mangifera macrocarpa, Gluta elegans, Campnosperma auriculatum and Swintonia floribunda. The mean density of Anacardiaceae within the study plots is 7.50±8.14 (mean±S.D.) per ha whereas the basal area (BA) calculated is 0.97 m(2)/0.40 ha. The importance value (IVi) for Anacardiaceae is 81%. The estimated total aboveground biomass (TAGB) for Anacardiaceae is 24.24 ton/0.40 ha. A total of 333 Anacardiaceae saplings with a DBH < 5 cm are recorded. These saplings have been identified as juveniles of the genera Gluta (9.99%), Swintonia (84.90%) and Mangifera (5.11%).

Matched MeSH terms: Algorithms
Fulltext The Phosphofructokinase and Pyruvate Kinase Genes in Apis andreniformis and Apis cerana indica: Exon Intron Organisation and Evolution

Shullia NI, Raffiudin R, Juliandi B

Trop Life Sci Res, 2019 Jan;30(1):89-107.
PMID: 30847035 DOI: 10.21315/tlsr2019.30.1.6

Genes related to carbohydrate metabolism have evolved rapidly in eusocial bees, including honey bees. However, the characterisation of carbohydrate metabolism genes has not been reported in Apis andreniformis or Apis cerana indica. This study aimed to characterise phosphofructokinase (PFK) and pyruvate kinase (PK) genes in these honey bee species and to analyse the evolution of the genus Apis using these genes. This study found the first data regarding A. andreniformis PFK and PK-like nucleotide sequences. A BLAST-n algorithm-based search showed that A. andreniformis and A. c. indica PFK and PK genes were homologous with those of Apis florea and Apis cerana cerana from Korea, respectively. Multiple alignments of PFKs from five Apis species showed many exon gains and losses, but only one among the PKs. Thus, the exon-intron organisation of the PK genes may be more conserved compare with that of the PFKs. Another evolutionary pattern indicated that more nucleotide substitutions occurred in Apis' PK than PFK genes. Deduced PFK amino acid sequences revealed a PFK consensus pattern of 19 amino acids, while the deduced PK amino acid sequences were predicted to have barrel and alpha/beta domains. Based on these two metabolism-related genes, The Neighbour-joining and Maximum likelihood phylogenetic trees are congruent and revealed that the A. andreniformis and A. florea group were in the basal position. Apis mellifera, A. cerana, and Apis dorsata formed a monophyletic clade, although the positions of A. mellifera and A. dorsata were different in the nucleotide- and amino acid-based phylogenetic trees.

Matched MeSH terms: Algorithms
The effectiveness of pharmacist-led educational model in adult patients with allergic rhinitis: a single-center randomized control trial protocol (AR-PRISE RCT)

Chew CC, Lim XJ, Letchumanan P, George D, Rajan P, Chong CP

Trials, 2024 Apr 25;25(1):279.
PMID: 38664701 DOI: 10.1186/s13063-024-08111-y

BACKGROUND: Allergic rhinitis is a chronic respiratory disorder that significantly impacts patients' quality of life (QoL) and work performance. Pharmacists are recognized as suitable professionals to provide patient education and pharmaceutical care for managing allergic rhinitis patients. However, local clinical practice guidelines, particularly regarding pharmaceutical care in public healthcare institutions, are lacking. This study protocol outlines a randomized controlled trial (RCT) designed to evaluate the effectiveness of a pharmacist-led educational model (AR-PRISE Model) in managing allergic rhinitis in adult patients compared to standard pharmaceutical care. The AR-PRISE model delivers patient educational material and a pharmaceutical care algorithm.
METHOD: This is a 6-month, single-center, prospective, randomized, two-arm, and parallel-group controlled trial. The trial recruits patients attending the otorhinolaryngology clinics of a tertiary referral hospital. Participants are randomized into control or intervention groups in a 1:1 ratio using permuted block randomization. The total number of participants estimated is 154, with each group requiring 77 participants. The control group receives standard pharmaceutical care, while the intervention group receives pharmacist-led education according to the AR-PRISE model. Both groups are assessed for middle turbinate endoscopy findings, disease severity, knowledge level, symptom control, medication adherence, and QoL at baseline and the end-of-study follow-up (day 180 ± 7). Depending on feasibility, intermediate follow-ups are conducted on days 60 ± 7 and 120 ± 7, either virtually or face-to-face. During intermediate follow-ups, participants are assessed for symptom control, medication adherence, and QoL. The intention-to-treat analysis includes all participants assigned to each group. An independent T-test compares the mean difference in knowledge level between the two groups. A two-way repeated measures ANOVA analysis is employed to determine between-group differences for scores of symptom control, adherence rate, and QoL. A P-value

Matched MeSH terms: Algorithms
Fulltext A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Rahman H, Khan AR, Sadiq T, Farooqi AH, Khan IU, Lim WH

Tomography, 2023 Dec 05;9(6):2158-2189.
PMID: 38133073 DOI: 10.3390/tomography9060169

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Matched MeSH terms: Algorithms
Methods for Predicting Warfarin Dose Requirements

Saffian SM, Wright DF, Roberts RL, Duffull SB

Ther Drug Monit, 2015 Aug;37(4):531-8.
PMID: 25549208 DOI: 10.1097/FTD.0000000000000177

The aim of this study was to compare the predictive performance of different warfarin dosing methods.

Matched MeSH terms: Algorithms*
Fulltext Application of pattern recognition tools for classifying acute coronary syndrome: an integrated medical modeling

Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I

Theor Biol Med Model, 2013 Sep 18;10:57.
PMID: 24044669 DOI: 10.1186/1742-4682-10-57

OBJECTIVE: The classification of Acute Coronary Syndrome (ACS), using artificial intelligence (AI), has recently drawn the attention of the medical researchers. Using this approach, patients with myocardial infarction can be differentiated from those with unstable angina. The present study aims to develop an integrated model, based on the feature selection and classification, for the automatic classification of ACS.
METHODS: A dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.
RESULTS: The performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.
CONCLUSION: The results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.

Matched MeSH terms: Algorithms
Fulltext Critical product features' identification using an opinion analyzer

Shamim A, Balakrishnan V, Tahir M, Shiraz M

ScientificWorldJournal, 2014;2014:340583.
PMID: 25506612 DOI: 10.1155/2014/340583

The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer's reviews. Online consumer's reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers' reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers' reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers' feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly.

Matched MeSH terms: Algorithms
Fulltext Coefficient bounds for some families of starlike and convex functions of reciprocal order

Arif M, Darus M, Raza M, Khan Q

ScientificWorldJournal, 2014;2014:989640.
PMID: 25506621 DOI: 10.1155/2014/989640

The aim of the present paper is to investigate coefficient estimates, Fekete-Szegő inequality, and upper bound of third Hankel determinant for some families of starlike and convex functions of reciprocal order.

Matched MeSH terms: Algorithms*
Fulltext A novel N-input voting algorithm for X-by-wire fault-tolerant systems

Karimi A, Zarafshan F, Al-Haddad SA, Ramli AR

ScientificWorldJournal, 2014;2014:672832.
PMID: 25386613 DOI: 10.1155/2014/672832

Voting is an important operation in multichannel computation paradigm and realization of ultrareliable and real-time control systems that arbitrates among the results of N redundant variants. These systems include N-modular redundant (NMR) hardware systems and diversely designed software systems based on N-version programming (NVP). Depending on the characteristics of the application and the type of selected voter, the voting algorithms can be implemented for either hardware or software systems. In this paper, a novel voting algorithm is introduced for real-time fault-tolerant control systems, appropriate for applications in which N is large. Then, its behavior has been software implemented in different scenarios of error-injection on the system inputs. The results of analyzed evaluations through plots and statistical computations have demonstrated that this novel algorithm does not have the limitations of some popular voting algorithms such as median and weighted; moreover, it is able to significantly increase the reliability and availability of the system in the best case to 2489.7% and 626.74%, respectively, and in the worst case to 3.84% and 1.55%, respectively.

Matched MeSH terms: Algorithms*
Fulltext Advanced mobility handover for mobile IPv6 based wireless networks

Safa Sadiq A, Fisal NB, Ghafoor KZ, Lloret J

ScientificWorldJournal, 2014;2014:602808.
PMID: 25614890 DOI: 10.1155/2014/602808

We propose an Advanced Mobility Handover scheme (AMH) in this paper for seamless mobility in MIPv6-based wireless networks. In the proposed scheme, the mobile node utilizes a unique home IPv6 address developed to maintain communication with other corresponding nodes without a care-of-address during the roaming process. The IPv6 address for each MN during the first round of AMH process is uniquely identified by HA using the developed MN-ID field as a global permanent, which is identifying uniquely the IPv6 address of MN. Moreover, a temporary MN-ID is generated by access point each time an MN is associated with a particular AP and temporarily saved in a developed table inside the AP. When employing the AMH scheme, the handover process in the network layer is performed prior to its default time. That is, the mobility handover process in the network layer is tackled by a trigger developed AMH message to the next access point. Thus, a mobile node keeps communicating with the current access point while the network layer handover is executed by the next access point. The mathematical analyses and simulation results show that the proposed scheme performs better as compared with the existing approaches.

Matched MeSH terms: Algorithms
Fulltext An adaptive handover prediction scheme for seamless mobility based wireless networks

Sadiq AS, Fisal NB, Ghafoor KZ, Lloret J

ScientificWorldJournal, 2014;2014:610652.
PMID: 25574490 DOI: 10.1155/2014/610652

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Matched MeSH terms: Algorithms*
Fulltext Computational intelligence and metaheuristic algorithms with applications

Yang XS, Chien SF, Ting TO

ScientificWorldJournal, 2014;2014:425853.
PMID: 25610904 DOI: 10.1155/2014/425853
Matched MeSH terms: Algorithms
Fulltext Investigation of a new handover approach in LTE and WiMAX

Hindia MN, Reza AW, Noordin KA

ScientificWorldJournal, 2014;2014:246206.
PMID: 25379524 DOI: 10.1155/2014/246206

Nowadays, one of the most important challenges in heterogeneous networks is the connection consistency between the mobile station and the base stations. Furthermore, along the roaming process between the mobile station and the base station, the system performance degrades significantly due to the interferences from neighboring base stations, handovers to inaccurate base station and inappropriate technology selection. In this paper, several algorithms are proposed to improve mobile station performance and seamless mobility across the long-term evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX) technologies, along with a minimum number of redundant handovers. Firstly, the enhanced global positioning system (GPS) and the novel received signal strength (RSS) prediction approaches are suggested to predict the target base station accurately. Then, the multiple criteria with two thresholds algorithm is proposed to prioritize the selection between LTE and WiMAX as the target technology. In addition, this study also covers the intercell and cochannel interference reduction by adjusting the frequency reuse ratio 3 (FRR3) to work with LTE and WiMAX. The obtained results demonstrate high next base station prediction efficiency and high accuracy for both horizontal and vertical handovers. Moreover, the received signal strength is kept at levels higher than the threshold, while maintaining low connection cost and delay within acceptable levels. In order to highlight the combination of the proposed algorithms' performance, it is compared with the existing RSS and multiple criteria handover decision algorithms.

Matched MeSH terms: Algorithms*
Fulltext Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization

Adam A, Shapiai MI, Tumari MZ, Mohamad MS, Mubin M

ScientificWorldJournal, 2014;2014:973063.
PMID: 25243236 DOI: 10.1155/2014/973063

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

Matched MeSH terms: Algorithms*
Fulltext A modified active appearance model based on an adaptive artificial bee colony

Abdulameer MH, Sheikh Abdullah SN, Othman ZA

ScientificWorldJournal, 2014;2014:879031.
PMID: 25165748 DOI: 10.1155/2014/879031

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Matched MeSH terms: Algorithms*
Fulltext An alternate method to springback compensation for sheet metal forming

Siswanto WA, Anggono AD, Omar B, Jusoff K

ScientificWorldJournal, 2014;2014:301271.
PMID: 25165738 DOI: 10.1155/2014/301271

The aim of this work is to improve the accuracy of cold stamping product by accommodating springback. This is a numerical approach to improve the accuracy of springback analysis and die compensation process combining the displacement adjustment (DA) method and the spring forward (SF) algorithm. This alternate hybrid method (HM) is conducted by firstly employing DA method followed by the SF method instead of either DA or SF method individually. The springback shape and the target part are used to optimize the die surfaces compensating springback. The hybrid method (HM) algorithm has been coded in Fortran and tested in two- and three-dimensional models. By implementing the HM, the springback error can be decreased and the dimensional deviation falls in the predefined tolerance range.

Matched MeSH terms: Algorithms*
Fulltext A new graph-based molecular descriptor using the canonical representation of the molecule

Hentabli H, Saeed F, Abdo A, Salim N

ScientificWorldJournal, 2014;2014:286974.
PMID: 25140330 DOI: 10.1155/2014/286974

Molecular similarity is a pervasive concept in drug design. The basic idea underlying molecular similarity is the similar property principle, which states that structurally similar molecules will exhibit similar physicochemical and biological properties. In this paper, a new graph-based molecular descriptor (GBMD) is introduced. The GBMD is a new method of obtaining a rough description of 2D molecular structure in textual form based on the canonical representations of the molecule outline shape and it allows rigorous structure specification using small and natural grammars. Simulated virtual screening experiments with the MDDR database show clearly the superiority of the graph-based descriptor compared to many standard descriptors (ALOGP, MACCS, EPFP4, CDKFP, PCFP, and SMILE) using the Tanimoto coefficient (TAN) and the basic local alignment search tool (BLAST) when searches were carried.

Matched MeSH terms: Algorithms

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