Displaying publications 21 - 40 of 125 in total

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  1. Vision-based lane departure warning framework
    Em PP, Hossen J, Fitrian I, Wong EK
    Heliyon, 2019 Aug;5(8):e02169.
    PMID: 31440587 DOI: 10.1016/j.heliyon.2019.e02169
    Collisions arising from lane departures have contributed to traffic accidents causing millions of injuries and tens of thousands of casualties per year worldwide. Many related studies had shown that single vehicle lane departure crashes accounted largely in road traffic deaths that results from drifting out of the roadway. Hence, automotive safety has becoming a concern for the road users as most of the road casualties occurred due to driver's fallacious judgement of vehicle path. This paper proposes a vision-based lane departure warning framework for lane departure detection under daytime and night-time driving environments. The traffic flow and conditions of the road surface for both urban roads and highways in the city of Malacca are analysed in terms of lane detection rate and false positive rate. The proposed vision-based lane departure warning framework includes lane detection followed by a computation of a lateral offset ratio. The lane detection is composed of two stages: pre-processing and detection. In the pre-processing, a colour space conversion, region of interest extraction, and lane marking segmentation are carried out. In the subsequent detection stage, Hough transform is used to detect lanes. Lastly, the lateral offset ratio is computed to yield a lane departure warning based on the detected X-coordinates of the bottom end-points of each lane boundary in the image plane. For lane detection and lane departure detection performance evaluation, real-life datasets for both urban roads and highways in daytime and night-time driving environments, traffic flows, and road surface conditions are considered. The experimental results show that the proposed framework yields satisfactory results. On average, detection rates of 94.71% for lane detection rate and 81.18% for lane departure detection rate were achieved using the proposed frameworks. In addition, benchmark lane marking segmentation methods and Caltech lanes dataset were also considered for comparison evaluation in lane detection. Challenges to lane detection and lane departure detection such as worn lane markings, low illumination, arrow signs, and occluded lane markings are highlighted as the contributors to the false positive rates.
    Matched MeSH terms: Benchmarking
  2. Fulltext Latent based temporal optimization approach for improving the performance of collaborative filtering
    Al-Hadi IAA, Sharef NM, Sulaiman MN, Mustapha N, Nilashi M
    PeerJ Comput Sci, 2020;6:e331.
    PMID: 33816980 DOI: 10.7717/peerj-cs.331
    Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers' ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers' drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.
    Matched MeSH terms: Benchmarking
  3. Fulltext Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer
    Rahman MA, Muniyandi RC, Albashish D, Rahman MM, Usman OL
    PeerJ Comput Sci, 2021;7:e344.
    PMID: 33816995 DOI: 10.7717/peerj-cs.344
    Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.
    Matched MeSH terms: Benchmarking
  4. Fulltext Improving the performance of opportunistic routing using min-max range and optimum energy level for relay node selection in wireless sensor networks
    Yee PL, Mehmood S, Almogren A, Ali I, Anisi MH
    PeerJ Comput Sci, 2020;6:e326.
    PMID: 33816976 DOI: 10.7717/peerj-cs.326
    Opportunistic routing is an emerging routing technology that was proposed to overcome the drawback of unreliable transmission, especially in Wireless Sensor Networks (WSNs). Over the years, many forwarder methods were proposed to improve the performance in opportunistic routing. However, based on existing works, the findings have shown that there is still room for improvement in this domain, especially in the aspects of latency, network lifetime, and packet delivery ratio. In this work, a new relay node selection method was proposed. The proposed method used the minimum or maximum range and optimum energy level to select the best relay node to forward packets to improve the performance in opportunistic routing. OMNeT++ and MiXiM framework were used to simulate and evaluate the proposed method. The simulation settings were adopted based on the benchmark scheme. The evaluation results showed that our proposed method outperforms in the aspect of latency, network lifetime, and packet delivery ratio as compared to the benchmark scheme.
    Matched MeSH terms: Benchmarking
  5. Fulltext Hyperledger Fabric Blockchain for Securing the Edge Internet of Things
    Honar Pajooh H, Rashid M, Alam F, Demidenko S
    Sensors (Basel), 2021 Jan 07;21(2).
    PMID: 33430274 DOI: 10.3390/s21020359
    Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems' scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.
    Matched MeSH terms: Benchmarking
  6. Fulltext MARKOWITZ THEORY IN PORTFOLIO OPTIMIZATION FOR HEAVY TAILED ASSETS
    WONG GHEE CHING, CHE MOHD IMRAN CHE TAIB
    UMT Journal of Undergraduate Research, 2019;1(3):1-14.
    MyJurnal
    This paper aims at solving an optimization problem in the presence of heavy tail behavior of financial assets. The question of minimizing risk subjected to a certain expected return or maximizing return fora given expected risk are two objective functions to be solved using Markowitz model. The Markowitz based strategies namely the mean variance portfolio, minimum variance portfolio and equally weighted portfolio are proposed in conjunction with mean and variance analysis of the portfolio. The historical prices of stocks traded at Bursa Malaysia are used for empirical analysis. We employed CAPM in order to investigate the performance of the Markowitz model which was benchmarked with risk adjusted KLSE Composite Index. We performed a backtesting study of portfolio optimization techniques defined under modern portfolio theory in order to find the optimal portfolio. Our findings showthat the mean variance portfolio outperformed the other two strategies in termsof performance of investment for heavy tailed assets.
    Matched MeSH terms: Benchmarking
  7. Fulltext Performance analysis of a commodity-class linux cluster for computational chemistry applications
    Omar, N.Y.M., Rahman, N.A., Zain, S.M.
    ASM Science Journal, 2009;3(1):77-90.
    MyJurnal
    Computational chemistry is a discipline that concerns the computing of physical and chemical properties of atoms and molecules using the fundamentals of quantum mechanics. The expense of computational chemistry calculations is significant and limited by available computational capabilities. The use of high-performance computing clusters alleviate such calculations. However, as high-performance computing (HPC) clusters have always required a balance between four major factors: raw computing power, memory size, I/O capacity, and communication capacity. In this paper, we present the results of standard HPC benchmarks in order to help assess the performance characteristics of the various hardware and software components of a home-built commodity-class Linux cluster. We optimized a range of TCP/MPICH parameters and achieved a maximum MPICH bandwidth of 666 Mbps. The bandwidth and latency of GA put/get operations were better than the corresponding MPICH send/receive ones. We also examined the NFS, PVFS2, and Lustre parallel filesystems and Lustre provided the best read/write bandwidths with more than 90% of those of the local filesystem.
    Matched MeSH terms: Benchmarking
  8. Fulltext Content-based image retrieval using colour and shape fused features
    Mas Rina Mustaffa, Fatimah Ahmad, Ramlan Mahmod, Shyamala Doraisamy
    Pertanika Journal of Science & Technology, 2013;21(1):161-168.
    MyJurnal
    Multi-feature methods are able to contribute to a more effective method compared to single-feature
    methods since feature fusion methods will be able to close the gap that exists in the single-feature
    methods. This paper presents a feature fusion method, which focuses on extracting colour and shape features for content-based image retrieval (CBIR). The colour feature is extracted based on the proposed Multi-resolution Joint Auto Correlograms (MJAC), while the shape information is obtained through the proposed Extended Generalised Ridgelet-Fourier (EGRF). These features are fused together through a proposed integrated scheme. The feature fusion method has been tested on the SIMPLIcity image database, where several retrieval measurements are utilised to compare the effectiveness of the proposed method with few other comparable methods. The retrieval results show that the proposed Integrated Colour-shape (ICS) descriptor has successfully obtained the best overall retrieval performance in all the retrieval measurements as compared to the benchmark methods, which include precision (53.50%), precision at 11 standard recall levels (52.48%), and rank (17.40).
    Matched MeSH terms: Benchmarking
  9. Fulltext Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks
    Liaqat M, Gani A, Anisi MH, Ab Hamid SH, Akhunzada A, Khan MK, et al.
    PLoS One, 2016 Sep 22;11(9):e0161340.
    PMID: 27658194 DOI: 10.1371/journal.pone.0161340
    A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results.
    Matched MeSH terms: Benchmarking
  10. Fulltext Statistical assessment of performance of Malaysian track and field athletes
    Ballard, Robert J., Jad Adrian Washif, Richards, Andre C.
    Movement Health & Exercise, 2016;5(1):1-11.
    MyJurnal
    The purpose of this study was to assess the foundation depth of track and field events and objectively identify if there had been a significant advancement of performance in athletics at a foundation level. Data was taken from all 45 athletics events (23 male, 22 female) over the last seven biannual National Games from 2002 to 2014. The performances for the top five finalists across each event were analyzed. A correlation co-efficient was calculated to assess the strength of linear performance relationship over time. T-tests were performed to assess mean differences across high prospect events versus major international benchmarks. Using r = 0.70 as a high correlation, only five events (men’s 400 m, 400 m hurdles, high jump, and both men’s and women’s hammer throw) out of 41 individual events demonstrated strong positive linear relationships over the assessment period. Only men’s high jump and women’s hammer throw had a non-significant difference (p = > 0.05) when compared with the means performance measure at the two Southeast Asian Games, indicating the events’ degree of capacity to compete at an international level. In comparison to higher level competitions, men’s high jump and women’s hammer throw also demonstrated fragility. The performance gap between the National Games and comparable international event was very large, ranging from 5.3 to 71.0%. Overall, there appears to be a trivial or stagnant trend for many athletics events, which has been unable to create a foundation needed for developing consistent elite performance. Taking into account this data, consideration of the development of new intervention action plans within the overall strategy should be determined and implemented.
    Matched MeSH terms: Benchmarking
  11. Fulltext Performance evaluation of a mobile road traffic infraction registration system through benchmark
    Arasteh-Rad, H., Khairulmizam Samsudin, Abdul Rahman Ramli, Mohammad Ali Tavallaie
    Pertanika Journal of Science & Technology, 2012;20(1):63-80.
    MyJurnal
    The rapid development of roads and the increasing number of vehicles have complicated road traffic enforcement in many countries due to limited resources of the traffic police, specifically when traffic infraction registration is done manually. The efficiency of the traffic police can be improved by a computer-based method. This study focused on mobile traffic infraction registration system benchmarking which is used to evaluate the server performance under load. The study attempts to provide a clear guideline for the performance evaluation of mobile road traffic infraction registration system, whereby the traffic police can make decision based on them to migrate from the manual-method toward computer-based method. A closed form of benchmark tool was used for the evaluation of the system performance. The tool was configured to imitate ramp scenarios, and statistics were gathered. The server was monitored at different times and works. Contributing factors include bottleneck, traffic, and response time, which are related with criteria and measurements. The system resource was also monitored for the tests.
    Matched MeSH terms: Benchmarking
  12. Fulltext A study on the microclimatic performance of courtyard and non-courtyard residential buildings in hot-arid climate
    Markus Bulus, Lim, Yaik-Wah, Malsiah Hamid
    Journal of Advanced Research in Applied Sciences and Engineering Technology, 2017;9(1):1-13.
    MyJurnal
    Scholars have opined that the courtyard is a passive architectural design element and
    that it can act as a microclimate modifier provided that its design requirements are not
    ignored. But despite the assertions, empirical studies on the microclimatic
    performance of a fully enclosed courtyard house and the non-courtyard house seems
    to be deficient, and the assumption that the Courtyard is a passive architectural design
    element needs to be substantiated. Therefore, the purpose of this study is to
    investigate the microclimatic performance of a fully enclosed courtyard and noncourtyard
    residential buildings. The main objective is to compare their microclimatic
    performances in other to draw a conclusion on the best option. Three Hobo Weather
    Data Loggers were used to collect climatic data in the buildings, and the third one was
    situated in the outdoor area as a benchmark. The climatic variables investigated are;
    air temperature and relative humidity. The fully enclosed courtyard residential building
    is seen to have a better air temperature difference of 2 oC to 4 oC and the relative
    humidity of 2 % to 6 %. In conclusion, the fully enclosed courtyard residential building
    has confirmed a more favorable microclimatic performance, and future studies
    towards its optimization are recommended.
    Matched MeSH terms: Benchmarking
  13. An accelerated over-relaxation quarter-sweep point iterative method for two-dimensional poisson equation
    Rakhimov SI, Mohamed Othman
    Sains Malaysiana, 2009;38.
    Iterative methods, particularly over-relaxation methods, are efficiently and frequently used to solve large systems of linear equations, because in the solutions of partial differential equations, these methods are applied to systems which are resulted from different iterative schemes to discrete equations. In this paper we formulate an accelerated over-relaxation (AOR) method with the quarter-sweep iterative scheme applied to the Poisson equation. To benchmark the new method we conducted experiments by comparing it with the previous AOR methods based on full- and half-sweep iterative schemes. The results of the experiments and the estimation of the computational complexity of the methods proved the superiority of the new method.
    Matched MeSH terms: Benchmarking
  14. Fulltext Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization
    Al-Khaleefa AS, Ahmad MR, Isa AAM, Esa MRM, Aljeroudi Y, Jubair MA, et al.
    Sensors (Basel), 2019 May 25;19(10).
    PMID: 31130657 DOI: 10.3390/s19102397
    Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.
    Matched MeSH terms: Benchmarking
  15. Trends in sustainable logistics in major cities in China
    Lan S, Tseng ML, Yang C, Huisingh D
    Sci Total Environ, 2020 Apr 10;712:136381.
    PMID: 31940512 DOI: 10.1016/j.scitotenv.2019.136381
    "Smart cities" have become the development direction pursued by city leaders to address challenges related to rapid growth in urban areas. The sustainable development of the logistics sector has important practical significance for the evolution of smart cities. This study assessed the inefficiency rate and total factor productivity (TFP) of logistics in 36 Chinese cities from 2006 to 2015. The directional distance function (DDF) and Luenberger productivity index analytical approaches were used to assess the relevant parameters. The results revealed that the logistics system inefficiency rate of the eastern region was much higher than that of the central and western regions, while that of the western region was slightly higher than that of the central region. This study identified the main constraints of the logistics TFP in different regions in China. This finding is used to promote policy-making and investment planning to improve China's competitive advantage. The results documented that the central region of China needs to accelerate logistics reforms and use its location advantage of its location to form an organic connection with the eastern and western regions. Countries can use such metrics to take actions to improve their logistics performance, as such an improvement has a causal relationship with economic development.
    Matched MeSH terms: Benchmarking
  16. Fulltext Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment
    Lye GX, Cheng WK, Tan TB, Hung CW, Chen YL
    Sensors (Basel), 2020 Apr 08;20(7).
    PMID: 32276431 DOI: 10.3390/s20072098
    Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge-desire-intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users' beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.
    Matched MeSH terms: Benchmarking
  17. Fulltext Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid
    Singh N, Elamvazuthi I, Nallagownden P, Ramasamy G, Jangra A
    Sensors (Basel), 2020 May 25;20(10).
    PMID: 32466240 DOI: 10.3390/s20102992
    Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman-Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%-43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.
    Matched MeSH terms: Benchmarking
  18. Fulltext DeepNC: a framework for drug-target interaction prediction with graph neural networks
    Tran HNT, Thomas JJ, Ahamed Hassain Malim NH
    PeerJ, 2022;10:e13163.
    PMID: 35578674 DOI: 10.7717/peerj.13163
    The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index.
    Matched MeSH terms: Benchmarking
  19. Consensus-Based Development of a Global Registry for Traumatic Brain Injury: Establishment, Protocol, and Implementation
    Joannides AJ, Korhonen TK, Clark D, Gnanakumar S, Venturini S, Mohan M, et al.
    Neurosurgery, 2024 Feb 01;94(2):278-288.
    PMID: 37747225 DOI: 10.1227/neu.0000000000002661
    BACKGROUND AND OBJECTIVES: Global disparity exists in the demographics, pathology, management, and outcomes of surgically treated traumatic brain injury (TBI). However, the factors underlying these differences, including intervention effectiveness, remain unclear. Establishing a more accurate global picture of the burden of TBI represents a challenging task requiring systematic and ongoing data collection of patients with TBI across all management modalities. The objective of this study was to establish a global registry that would enable local service benchmarking against a global standard, identification of unmet need in TBI management, and its evidence-based prioritization in policymaking.

    METHODS: The registry was developed in an iterative consensus-based manner by a panel of neurotrauma professionals. Proposed registry objectives, structure, and data points were established in 2 international multidisciplinary neurotrauma meetings, after which a survey consisting of the same data points was circulated within the global neurotrauma community. The survey results were disseminated in a final meeting to reach a consensus on the most pertinent registry variables.

    RESULTS: A total of 156 professionals from 53 countries, including both high-income countries and low- and middle-income countries, responded to the survey. The final consensus-based registry includes patients with TBI who required neurosurgical admission, a neurosurgical procedure, or a critical care admission. The data set comprised clinically pertinent information on demographics, injury characteristics, imaging, treatments, and short-term outcomes. Based on the consensus, the Global Epidemiology and Outcomes following Traumatic Brain Injury (GEO-TBI) registry was established.

    CONCLUSION: The GEO-TBI registry will enable high-quality data collection, clinical auditing, and research activity, and it is supported by the World Federation of Neurosurgical Societies and the National Institute of Health Research Global Health Program. The GEO-TBI registry ( https://geotbi.org ) is now open for participant site recruitment. Any center involved in TBI management is welcome to join the collaboration to access the registry.

    Matched MeSH terms: Benchmarking
  20. Impact of Article Page Count and Number of Authors on Citations in Disability Related Fields: A Systematic Review Article
    Ahmed A, Adam M, Ghafar NA, Muhammad M, Ebrahim NA
    Iran J Public Health, 2016 Sep;45(9):1118-1125.
    PMID: 27957456
    BACKGROUND: Citation metrics and total publications in a field has become the gold standard for rating researchers and viability of a field. Hence, stimulating demand for citation has led to a search for useful strategies to improve performance metric index. Meanwhile, title, abstract and morphologic qualities of the articles attract researchers to scientific publications. Yet, there is relatively little understanding of the citation trend in disability related fields. We aimed to provide an insight into the factors associated with citation increase in this field. Additionally, we tried to know at what page number an article might appear attractive to disability researchers needs. Thus, our focus is placed on the article page count and the number of authors contributing to the fields per article.

    METHODS: To this end, we evaluated the quantitative characteristics of top cited articles in the fields with a total citation (≥50) in the Web of Science (WoS) database. Using one-way independent ANOVA, data extracted spanning a period of 1980-2015 were analyzed, while the non-parametric data analysis uses Kruskal-Walis test.

    RESULTS: Articles with 11 to 20 pages attract more citations followed by those within the range of zero to 10. Articles with upward 21 pages are the least cited. Surprisingly, articles with more than two authors are significantly (P<0.05) less cited and the citation decreases as the number of authors increased.

    CONCLUSION: Collaborative studies enjoy wider utilization and more citation, yet discounted merit of additional pages and limited collaborative research in disability field is revealed in this study.

    Matched MeSH terms: Benchmarking
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