A novel hamstring strain injury prevention system: post-match strength testing for secondary prevention in football
- Authors: Wollin, Martin , Thorborg, Kristian , Drew, Michael , Pizzari, Tania
- Date: 2019
- Type: Text , Journal article , Editorial
- Relation: British Journal of Sports Medicine Vol. , no. (2019), p.
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API : an index for quantifying a scholar's academic potential
- Authors: Ren, Jing , Wang, Lei , Wang, Kailai , Yu, Shuo , Hou, Mingliang , Lee, Ivan , Kong, Xiangje , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 178675-178684
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- Description: In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, ${h}$-index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the Academic Potential Index (API) to quantify scholar's academic potential. Furthermore, an algorithm is devised to calculate the value of API. It should be noted that API is a dynamic index throughout scholar's academic career. By applying API to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars' academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed API can achieve superior performance in identifying potential scholars compared with three baseline methods. © 2019 IEEE.
Classifying transformer winding deformation fault types and degrees using FRA based on support vector machine
- Authors: Liu, Jiangnan , Zhao, Zhongyong , Tang, Chao , Yao, Chenguo , Li, Chengxiang , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 112494-112504
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- Description: As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer's failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.
Controlled ecological evaluation of an implemented exercise-training programme to prevent lower limb injuries in sport : Population-level trends in hospital-treated injuries
- Authors: Finch, Caroline , Gray, Shannon , Akram, Muhammad , Donaldson, Alex , Lloyd, David , Cook, Jill
- Date: 2019
- Type: Text , Journal article
- Relation: British Journal of Sports Medicine Vol. 53, no. 8 (2019), p. 487-492
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- Description: Objective Exercise-training programmes have reduced lower limb injuries in trials, but their population-level effectiveness has not been reported in implementation trials. This study aimed to demonstrate that routinely collected hospital data can be used to evaluate population-level programme effectiveness. Method A controlled ecological design was used to evaluate the effect of FootyFirst, an exercise-training programme, on the number of hospital-treated lower limb injuries sustained by males aged 16-50 years while participating in community-level Australian Football. FootyFirst was implemented with a € support' (FootyFirst+S) or a € without support' (FootyFirst+NS) in different geographic regions of Victoria, Australia: 22 clubs in region 1: FootyFirst+S in 2012/2013; 25 clubs in region 2: FootyFirst+NS in 2012/2013; 31 clubs region 3: control in 2012, FootyFirst+S in 2013. Interrupted time-series analysis compared injury counts across regions and against trends in the rest of Victoria. Results After 1 year of FootyFirst+S, there was a non-statistically significant decline in the number of lower limb injuries in region 1 (2012) and region 3 (2013); this was not maintained after 2 years in region 1. Compared with before FootyFirst in 2006-2011, injury count changes at the end of 2013 were: region 1: 20.0% reduction (after 2 years support); region 2: 21.5% increase (after 2 years without support); region 3: 21.8% increase (after first year no programme, second year programme with support); rest of Victoria: 12.6% increase. Conclusion Ecological analyses using routinely collected hospital data show promise as the basis of population-level programme evaluation. The implementation and sustainability of sports injury prevention programmes at the population-level remains challenging.
Diagnosing transformer winding deformation faults based on the analysis of binary image obtained from FRA signature
- Authors: Zhao, Zhongyong , Yao, Chenguo , Tang, Chao , Li, Chengxiang , Yan, Fayou , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 40463-40474
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- Description: Frequency response analysis (FRA) has been widely accepted as a diagnostic tool for power transformer winding deformation faults. Typically, both amplitude-frequency and phase-frequency signatures are obtained by an FRA analyzer. However, most existing FRA analyzers use only the information on amplitude-frequency signature, while phase-frequency information is neglected. It is also found that in some cases, the diagnostic results obtained by FRA amplitude-frequency signatures do not comply with some hard evidence. This paper introduces a winding deformation diagnostic method based on the analysis of binary images obtained from FRA signatures to improve FRA outcomes. The digital image processing technique is used to process the binary image and obtain a diagnostic indicator, to arrive at an outcome for interpreting winding faults with improved accuracy.
Dual mechanical port machine based hybrid electric vehicle using reduced switch converters
- Authors: Bizhani, Hamed , Yao, Gang , Muyeen, S. , Islam, Syed , Ben-Brahim, Lazhar
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 33665-33676
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- Description: Due to the increased environmental pollution, hybrid vehicles have attracted enormous attention in today's society. The two most important factors in designing these vehicles are size and weight. For this purpose, some researchers have presented the use of the dual-mechanical-port machine (DMPM) in hybrid electric vehicles (HEVs). This paper presents two modified converter topologies with a reduced number of switching devices for use on DMPM-based HEVs, with the goal of reducing the overall size and weight of the system. Beside the design of the DMPM in the series-parallel HEV structure along with the energy management unit, the conventional back-to-back (BB) converter is replaced with nine-switch (NS) and five-leg (FL) converters. These converters have never been examined for the DMPM-based HEV, and therefore, the objective of this paper is to reveal the operational characteristics and power flow mechanism of this machine using the NS and FL converters. The simulation analysis is carried out using MATLAB/Simulink considering all HEV operational modes. In addition, two proposed and the conventional converters are compared in terms of losses, maximum achievable voltages, required dc-link voltages, the rating of the components, and torque ripple, and finally, a recommendation is made based on the obtained results.
Identification of coherent generators by support vector clustering with an embedding strategy
- Authors: Babaei, Mehdi , Muyeen, S. , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 105420-105431
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- Description: Identification of coherent generators (CGs) is necessary for the area-based monitoring and protection system of a wide area power system. Synchrophasor has enabled smarter monitoring and control measures to be devised; hence, measurement-based methodologies can be implemented in online applications to identify the CGs. This paper presents a new framework for coherency identification that is based on the dynamic coupling of generators. A distance matrix that contains the dissimilarity indices between any pair of generators is constructed from the pairwise dynamic coupling of generators after the post-disturbance data are obtained by phasor measurement units (PMUs). The dataset is embedded in Euclidean space to produce a new dataset with a metric distance between the points, and then the support vector clustering (SVC) technique is applied to the embedded dataset to identify the final clusters of generators. Unlike other clustering methods that need a priori knowledge about the number of clusters or the parameters of clustering, this information is set in an automatic search procedure that results in the optimal number of clusters. The algorithm is verified by time-domain simulations of defined scenarios in 39 bus and 118 bus test systems. Finally, the clustering result of 39 bus systems is validated by cluster validity measures, and a comparative study investigates the efficacy of the proposed algorithm to cluster the generators with an optimal number of clusters and also its computational efficiency compared with other clustering methods.
Impact of load ramping on power transformer dissolved gas analysis
- Authors: Cui, Huize , Yang, Liuging , Li, Shengtao , Qu, Guanghao , Wang, Hao , Abu-Siada, Ahmed , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 170343-170351
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- Description: Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.
Multi-agent systems in ICT enabled smart grid : A status update on technology framework and applications
- Authors: Shawon, Mohammad , Muyeen, S. , Ghosh, Arindam , Islam, Syed , Baptista, Murilo
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 97959-97973
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- Description: Multi-agent-based smart grid applications have gained much attention in recent times. At the same time, information and communication technology (ICT) has become a crucial part of the smart grid infrastructure. The key intention of this work is to present a comprehensive review of the literature and technological frameworks for the application of multi-agent system (MAS) and ICT infrastructure usages in smart grid implementations. In the smart grid, agents are defined as intelligent entities with the ability to take decisions and acting flexibly and autonomously according to their built-in intelligence utilizing previous experiences. Whereas, ICT enables conventional grid turned into the smart grid through data and information exchange. This paper summarizes the multi-agent concept of smart grid highlighting their applications through a detailed and extensive literature survey on the related topics. In addition to the above, a particular focus has been put on the ICT standards, including IEC 61850 incorporating ICT with MAS. Finally, a laboratory framework concepts have been added highlighting the implementation of IEC 61850.
Optimization of an ultrasonic-assisted biodiesel production process from one genotype of rapeseed (TERI (OE) R-983) as a novel feedstock using response surface methodology
- Authors: Almasi, Sara , Ghobadian, Barat , Najafi, Gholam , Yusaf, Talal , Soufi, Masoud , Hoseini, Seyed
- Date: 2019
- Type: Text , Journal article
- Relation: Energies Vol. 12, no. 14 (2019), p. 1-14
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- Description: In recent years, due to the favorable climate conditions of Iran, the cultivation of rapeseed has increased significantly. The aim of this study was to investigate the possibility of biodiesel production from one genotype of rapeseed (TERI (OE) R-983). An ultrasonic approach was used in order to intensify the reaction. Response surface methodology (RSM) was applied to identify the optimum conditions of the process. The results of this research showed that the conversion of biodiesel was found to be 87.175% under the optimized conditions of a 4.63:1 molar ratio (methanol to oil), 56.50% amplitude, and 0.4 s pulses for a reaction time of 5.22 min. Increasing the operating conditions, such as the molar ratio from 4:1 to 5.5:1, amplitude from 50% to 72.5%, reaction time from 3 min to 7 min, and pulse from 0.4 s to 1 s, increased the FAME (fatty acid methyl esters) yield by approximately 4.5%, 2.3%, 1.2%, and 0.5%, respectively. The properties of the TERI (OE) R-983 methyl ester met the requirements of the biodiesel standard (ASTM D6751), indicating the potential of the produced biodiesel as an alternative fuel.
Performance assessment of a solar dryer system using small parabolic dish and alumina/oil nanofluid : simulation and experimental study
- Authors: Arkian, Amir , Najafi, Gholamhassan , Gorjian, Shiva , Loni, Reyhaneh , Bellos, Evangelos , Yusaf, Talal
- Date: 2019
- Type: Text , Journal article
- Relation: Energies Vol. 12, no. 24 (Dec 2019), p. 22
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- Description: In this study, a small dish concentrator with a cylindrical cavity receiver was experimentally investigated as the heat source of a dryer. The system was examined for operation with pure thermal oil and Al2O3/oil nanofluid as the working fluids in the solar system. Moreover, the design, the development, and the evaluation of the dried mint plant are presented in this work. Also, the solar dryer system was simulated by the SolidWorks and ANSYS CFX software. On the other side, the color histogram of the wet and dried mint samples based on the RGB method was considered. The results revealed that the different temperatures of the solar working fluids at the inlet and outlet of the cavity receiver showed similar trend data compared to the variation of the solar radiation during the experimental test. Moreover, it is found that the cavity heat gain and thermal efficiency of the solar system was improved by using the nanofluid as the solar working fluid. Furthermore, the required time for mint drying had decreased by increasing the drying temperature and increasing air speed. The highest drying time was measured equal to 320 min for the condition of the air speed equal to 0.5 m/s and the drying temperature of 30 degrees C. A good agreement was observed between the calculated numerical results and measured experimental data. Finally, based on the color histogram of the wet and dried mint samples, it was concluded that intensity amount of the red color of the mint increased with the drying process compared to intensity amount of the red color of the wet mint sample.
Reverse engineering genetic networks using nonlinear saturation kinetics
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2019
- Type: Text , Journal article
- Relation: BioSystems Vol. 182, no. (2019), p. 30-41
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- Description: A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions. Dynamics of such systems show nonlinear saturation kinetics which can be best modeled by Michaelis-Menten (MM) and Hill equations. Although MM equation is being widely used for modeling biochemical processes, it has been applied rarely for reverse engineering GRNs. In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs. In the coupled model, Michaelis-Menten constant associated with regulation by a gene is made invariant irrespective of the gene being regulated. The parameter estimation of the proposed model is carried out using an evolutionary optimization method, namely, trigonometric differential evolution (TDE). Subsequently, the model is further improved and the regulations of different genes by a given gene are made distinct by allowing varying values of Michaelis-Menten constants for each regulation. Apart from making the model more relevant biologically, the improvement results in a decoupled GRN model with fast estimation of model parameters. Further, to enhance exploitation of the search, we propose a local search algorithm based on hill climbing heuristics. A novel mutation operation is also proposed to avoid population stagnation and premature convergence. Real life benchmark data sets generated in vivo are used for validating the proposed model. Further, we also analyze realistic in silico datasets generated using GeneNetweaver. The comparison of the performance of proposed model with other existing methods shows the potential of the proposed model.
Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples
- Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol.56, no 4. (2020), p. 4415-4424
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- Description: Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.
The impacts of water pricing and non-pricing policies on sustainable water resources management : A case of Ghorveh Plain at Kurdistan province, Iran
- Authors: Asaadi, Mohammad , Mortazavi, Seyed , Zamani, Omid , Najafi, Gholam , Yusaf, Talal , Hoseini, Seyed
- Date: 2019
- Type: Text , Journal article
- Relation: Energies Vol. 12, no. 14 (2019), p. 1-16
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- Description: As with other regions of Iran, due to excessive extraction of groundwater for intense agricultural activity, Ghorveh plain, a water-scarce irrigation district in the west of Iran, has faced a serious water crisis during the last decade. The present study investigates the impacts of two scenario policies, namely, non-price policy (as a supply-oriented policy) and water pricing policies (as a demand-oriented policy) on agricultural sector of Ghorveh Plain, using positive mathematical programming (PMP). The model was calibrated by using farm-level data for the crop years in 2016-2017. Our findings indicate that applying water supply constraint policy will change the land use and cropping pattern to the crops with higher water productivity. The increase of water resource constraints can lead to the increase of water economic return which indicates a rising value of water resources shortage, warning the producers of the agriculture sector to allocate water to the crops with higher economic value under the water resources shortage conditions. In addition, the findings underline that in a situation where the price of irrigation water is low due to the low elasticity of water demand in the agriculture sector, formulating the economic instruments such as rising water prices does not solely suffice to achieve sustainable water resource management. However, mixed scenarios emphasized that the water distribution policies should be aligned with the increases in water cost.
The influence of emulsified water fuel containing fresh water microalgae on diesel engine performance, combustion, vibration and emission
- Authors: Al-Lwayzy, Saddam , Yusaf, Talal , Saleh, Khalid , Yousif, Belal
- Date: 2019
- Type: Text , Journal article
- Relation: Energies Vol. 12, no. 13 (2019), p. 1-17
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- Description: Microalgae is considered as an excellent potential renewable source of fuel in many forms including powder or slurry. A high percentage of emulsified water in the fuel is reported to reduce diesel engines’ emissions such as NOx, but that will compromise the engine output power. Using microalgae powder as an additive to enhance the emulsified water fuel heating value is the main objective of this work. Diesel engine combustion, vibration, performance and emissions were evaluated for pure cottonseed biodiesel (CS-B100), emulsified water 20% (vol.) in cottonseed biodiesel (CSB-E20) and emulsified water 20% (vol.) containing Fresh Water Microalgae Chlorella Vulgaris (FWM-CV) in cottonseed biodiesel (CSB-ME20). The emulsified water fuels showed a reduction in in-cylinder pressure, vibration, brake power, torque, exhaust gas temperature, CO2 and NOx, while BSFC and O2 were higher than the pure biodiesel (CS-B100). CSB-ME20 produced higher power and torque than CSB-E20 due to the presence of microalgae in the fuel that increased the energy content of the fuel.
Time-to-event analysis for sports injury research part 1 : Time-varying exposures
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 61-68
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- Description: Background: 'How much change in training load is too much before injury is sustained, among different athletes?' is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology. Aim: To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes. Content: Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills. Conclusion: To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data. © 2019 Author(s).
Time-to-event analysis for sports injury research part 2 : Time-varying outcomes
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 70-78
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- Description: Background: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. Content: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. Conclusion: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.
Too many rib ticklers? Injuries in Australian women's cricket (PhD Academy Award)
- Authors: Perera, Nirmala
- Date: 2019
- Type: Text , Journal article , Editorial Material
- Relation: British Journal of Sports Medicine Vol. 53, no. 22 (Nov 2019), p. 1436-1437
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A new cascaded multilevel inverter topology with galvanic isolation
- Authors: Hasan, Mubashwar , Abu-Siada, Ahmed , Islam, Syed , Dahidah, Mohamed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 54, no. 4 (2018), p. 3463-3472
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- Description: IEEE This paper presents a new compact three-phase cascaded multilevel inverter (CMLI) topology with reduced device count and high frequency magnetic link. The proposed topology overcomes the predominant limitation of separate DC power supplies, which CMLI always require. The high frequency magnetic link also provides a galvanic isolation between the input and output sides of the inverter, which is essential for various grid-connected applications. The proposed topology utilizes an asymmetric inverter configuration that consists of cascaded H-bridge cells and a conventional three-phase two-level inverter. A toroidal core is employed for the high frequency magnetic link to ensure compact size and high-power density. Compared with counterpart CMLI topologies available in the literatures, the proposed inverter has the advantage of utilizing the least number of power electronic components without compromising the overall performance, particularly when a high number of output voltage levels is required. The feasibility of the proposed inverter is confirmed through extensive simulation and experimentally validated studies.
A novel concept for three-phase cascaded multilevel inverter topologies
- Authors: Hasan, Mubashwar , Abu-Siada, Ahmed , Islam, Syed , Muyeen, S.
- Date: 2018
- Type: Text , Journal article
- Relation: Energies Vol. 11, no. 2 (2018), p. 1-16
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- Description: One of the key challenges in multilevel inverters (MLIs) design is to reduce the number of components used in the implementation while maximising the number of output voltage levels. This paper proposes a new concept that facilitates a device count reduction technique of existing cascaded MLIs. Moreover, the proposed concept can be utilised to extend existing single phase cascaded MLI topologies to three-phase structure without tripling the number of semiconductor components and input dc-supplies as per the current practice. The new generalized concept involves two stages; namely, cascaded stage and phase generator stage. The phase generator stage is a combination of a conventional three-phase two level inverter and three bi-directional switches while the cascaded stage can employ any existing cascaded topology. A laboratory prototype model is built and extensive experimental analyses are conducted to validate the feasibility of the proposed cascaded MLI concept.