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Full-Text Articles in Science and Technology Studies
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Faculty of Engineering and Information Sciences - Papers: Part B
The identification of protein-protein interaction (PPI) is one of the most important tasks to understand the biological functions and disease mechanisms. Although numerous databases of biological interactions have been published in debt to advanced high-throughput technology, the study of inter-species protein-protein interactions, especially between human and bacterium pathogens, remains an active yet challenging topic to harness computational models tackling the complex analysis and prediction tasks. In this paper, we comprehensively revisit the prediction task of human-bacterium protein-protein interactions (HB-PPI), which is a first ever endeavour to report an empirical evaluation in learning and predicting HB-PPI based on machine learning models. …
A Novel Monte Carlo-Based Neural Network Model For Electricity Load Forecasting, Binbin Yong, Zijian Xu, Jun Shen, Huaming Chen, Jianqing Wu, Fucun Li, Qingguo Zhou
A Novel Monte Carlo-Based Neural Network Model For Electricity Load Forecasting, Binbin Yong, Zijian Xu, Jun Shen, Huaming Chen, Jianqing Wu, Fucun Li, Qingguo Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated its great advantages. General Vector Machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we firstly review the basic concepts and implementation of GVM. Then we apply it in electricity load forecasting, which is based on the …
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun
Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun
Faculty of Engineering and Information Sciences - Papers: Part B
Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant preprocessing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early …
A New Data Driven Long-Term Solar Yield Analysis Model Of Photovoltaic Power Plants, Biplob Ray, Rakibuzzaman Shah, Md Rabiul Islam, Syed Islam
A New Data Driven Long-Term Solar Yield Analysis Model Of Photovoltaic Power Plants, Biplob Ray, Rakibuzzaman Shah, Md Rabiul Islam, Syed Islam
Faculty of Engineering and Information Sciences - Papers: Part B
Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV …
Model Predictive Control For A New Magnetic Linked Multilevel Inverter To Integrate Solar Photovoltaic Systems With The Power Grids, A M. Mahfuz-Ur-Rahman, Md Rabiul Islam, Kashem M. Muttaqi, Danny Sutanto
Model Predictive Control For A New Magnetic Linked Multilevel Inverter To Integrate Solar Photovoltaic Systems With The Power Grids, A M. Mahfuz-Ur-Rahman, Md Rabiul Islam, Kashem M. Muttaqi, Danny Sutanto
Faculty of Engineering and Information Sciences - Papers: Part B
The multilevel inverters are becoming increasingly popular for use in the grid integration of wind and photovoltaic (PV) power plants due to their higher voltage handling capability and the better output power quality. There are several types of multilevel inverters that have been proposed in the literature; among them the active neutral point clamp (ANPC) multilevel inverters have been drawing significant attention specially for solving the problems with other multilevel inverters. However, with the increase of number of levels, the ANPC requires more electronic switches and flying capacitors, by which the complexity and the cost increases. In this paper, an …
A Novel Empirical Heat Transfer Model For A Solar Thermal Storage Process Using Phase Change Materials, Yu Bie, Ming Li, Fei Chen, Grzegorz Krolczyk, Lin Yang, Zhixiong Li, Weihua Li
A Novel Empirical Heat Transfer Model For A Solar Thermal Storage Process Using Phase Change Materials, Yu Bie, Ming Li, Fei Chen, Grzegorz Krolczyk, Lin Yang, Zhixiong Li, Weihua Li
Faculty of Engineering and Information Sciences - Papers: Part B
Numerical and experimental analyses are often used to evaluate the solar thermal system with latent heat thermal energy storage (LHTES). However, the relationship between the numerical simulation and actual heat transfer process is still unclear. This work compares the simulated average temperature of two different phase change materials (PCMs) with experimental result at different operation conditions for the purpose of developing a temperature correction model. A novel empirical heat transfer model is then established to improve the simulation accuracy of PCM-based solar thermal systems. The contributions of this work include that (1) the system performance could be evaluated by the …
Leveraging Smote In A Two-Layer Model For Prediction Of Protein-Protein Interactions, Huaming Chen, Lei Wang, Chi-Hung Chi, Jun Shen
Leveraging Smote In A Two-Layer Model For Prediction Of Protein-Protein Interactions, Huaming Chen, Lei Wang, Chi-Hung Chi, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
The research of the mechanisms of infectious diseases between host and pathogens remains a hot topic. It takes stock of the interactions data between host and pathogens, including proteins and genomes, to facilitate the discoveries and prediction of underlying mechanisms. However, the incomplete protein-protein interactions data impediment the advances in this exploration and solicit the wet-lab experiments to examine and verify the latent interactions. Although there have been numerous studies trying to leverage the computational models, especially machine learning models, the performances of these models were not good enough to produce high-fidelity candidates of interactions data due to the nature …
A Theoretical Model For Total Suction Effects By Tree Roots, Udeshini Pathirage, Buddhima Indraratna, Muditha Pallewattha, Ana Heitor
A Theoretical Model For Total Suction Effects By Tree Roots, Udeshini Pathirage, Buddhima Indraratna, Muditha Pallewattha, Ana Heitor
Faculty of Engineering and Information Sciences - Papers: Part B
Strengthening soft and weak soil by way of root reinforcement is a well-known strategy that is adopted worldwide. In Australia, native gum trees remain evergreen throughout the year and have been utilised to stabilise transportation corridors by way of reinforcement provided by the roots and the suction generated within the root domain as a function of evapotranspiration through the canopy. A mature gum tree can induce a missive total suction pressure exceeding 30MPa through its root water and solute uptake in terms of matric plus osmotic suction. This cumulative effect of matric and osmotic suctions contributes to the overall shear …
Formulation Of A Model Predictive Control Algorithm To Enhance The Performance Of A Latent Heat Solar Thermal System, Gianluca Serale, Massimo Fiorentini, Alfonso Capozzoli, Paul Cooper, Marco Perino
Formulation Of A Model Predictive Control Algorithm To Enhance The Performance Of A Latent Heat Solar Thermal System, Gianluca Serale, Massimo Fiorentini, Alfonso Capozzoli, Paul Cooper, Marco Perino
Faculty of Engineering and Information Sciences - Papers: Part B
Model predictive control has proved to be a promising control strategy for improving the operational performance of multi-source thermal energy generation systems with the aim of maximising the exploitation of on-site renewable resources. This paper presents the formulation and implementation of a model predictive control strategy for the management of a latent heat thermal energy storage unit coupled with a solar thermal collector and a backup electric heater. The system uses an innovative Phase Change Material slurry for both the heat transfer fluid and storage media. The formulation of a model predictive controller of such a closed-loop solar system is …
Developing A Theoretical Model And Questionnaire Survey Instrument To Measure The Success Of Electronic Health Records In Residential Aged Care, Ping Yu, Siyu Qian
Developing A Theoretical Model And Questionnaire Survey Instrument To Measure The Success Of Electronic Health Records In Residential Aged Care, Ping Yu, Siyu Qian
Faculty of Engineering and Information Sciences - Papers: Part B
Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: …
Model Predictive Control (Mpc) For Enhancing Building And Hvac System Energy Efficiency: Problem Formulation, Applications And Opportunities, Gianluca Serale, Massimo Fiorentini, Alfonso Capozzoli, Daniele Bernardini, Alberto Bemporad
Model Predictive Control (Mpc) For Enhancing Building And Hvac System Energy Efficiency: Problem Formulation, Applications And Opportunities, Gianluca Serale, Massimo Fiorentini, Alfonso Capozzoli, Daniele Bernardini, Alberto Bemporad
Faculty of Engineering and Information Sciences - Papers: Part B
In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities …
A System Model For Personalized Medication Management (Mymediman)-The Consumers' Point Of View, Elena Vlahu-Gjorgievska, Khin Than Win, Willy Susilo
A System Model For Personalized Medication Management (Mymediman)-The Consumers' Point Of View, Elena Vlahu-Gjorgievska, Khin Than Win, Willy Susilo
Faculty of Engineering and Information Sciences - Papers: Part B
In this paper, we propose a design for a personalized medication management system model MyMediMan that provides medication information for different stakeholders. The focus of the paper is on the system's features and personalized information provided for the consumers as primary users of the proposed solution. The presented design introduces the consumers to different aspects of the medications they take and their overall health condition. The personalized information should increase the consumers' awareness about the positive benefits of taking the medications as well as the consequences that particular medication can have on their health condition. By obtaining this information, the …
Optimal Control Based Stiffness Identification Of An Ankle-Foot Orthosis Using A Predictive Walking Model, Manish Sreenivasa, Matthew Millard, Martin Felis, Katja Mombaur, Sebastian I. Wolf
Optimal Control Based Stiffness Identification Of An Ankle-Foot Orthosis Using A Predictive Walking Model, Manish Sreenivasa, Matthew Millard, Martin Felis, Katja Mombaur, Sebastian I. Wolf
Faculty of Engineering and Information Sciences - Papers: Part B
Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque …
Statistical Shape Model Generation Using Diffeomorphic Surface Registration, Jiaqi Wu, Guangxu Li, Huimin Lu, Hyoung Kim, Philip O. Ogunbona
Statistical Shape Model Generation Using Diffeomorphic Surface Registration, Jiaqi Wu, Guangxu Li, Huimin Lu, Hyoung Kim, Philip O. Ogunbona
Faculty of Engineering and Information Sciences - Papers: Part B
Statistical shape modelling is an efficient and robust method for medical image segmentation in computer-aided diagnosis. The key step in building a statistical shape model is to find corresponding landmarks in each instance of a training set. In this paper, a novel landmark correspondence estimation method that uses edge collapse surface simplification and the sphere registration is proposed. All the landmarks are selected and transformed by spherical conformal mapping from the instances of the training set and the associated correspondence are automatically found on the spheres. We applied our method on 21 cases of 3-D right lung shapes. The results …
Aligning Business Strategy With It Strategy From Business Model To Enterprise In Saudi Arabia Public Sector, Abdulaziz Alghazi, Mengxiang Li, Jun Shen, Samuel Fosso Wamba
Aligning Business Strategy With It Strategy From Business Model To Enterprise In Saudi Arabia Public Sector, Abdulaziz Alghazi, Mengxiang Li, Jun Shen, Samuel Fosso Wamba
Faculty of Engineering and Information Sciences - Papers: Part B
Over the last decades researchers seem to have big concerns on the complexity of business-IT alignment with the government organizational strategy goals. The Saudi Arabian (SA) government is one of the world’s governments that has launched the national government strategy for 2030 to all public sectors to enhance performance. This research proposal is aimed at investigating the adoption performance of alignment strategy incorporated by the SA government in order to benefit of IT infrastructure and achieve high performance, quality of service (QoS) and return of investment (ROI). This study will use mixed method design combines both qualitative and quantitative methods …
The Effect Of Imputing Missing Clinical Attribute Values On Training Lung Cancer Survival Prediction Model Performance, Mohamed S. Barakat, Matthew Field, Aditya K. Ghose, David Stirling, Lois C. Holloway, Shalini K. Vinod, Andre Dekker, David Thwaites
The Effect Of Imputing Missing Clinical Attribute Values On Training Lung Cancer Survival Prediction Model Performance, Mohamed S. Barakat, Matthew Field, Aditya K. Ghose, David Stirling, Lois C. Holloway, Shalini K. Vinod, Andre Dekker, David Thwaites
Faculty of Engineering and Information Sciences - Papers: Part B
According to the estimations of the World Health Organization and the International Agency for Research in Cancer, lung cancer is the most common cause of death from cancer worldwide. The last few years have witnessed a rise in the attention given to the use of clinical decision support systems in medicine generally and in cancer in particular. These can predict patients' likelihood of survival based on analysis of and learning from previously treated patients. The datasets that are mined for developing clinical decision support functionality are often incomplete, which adversely impacts the quality of the models developed and the decision …
Motion Optimization And Parameter Identification For A Human And Lower Back Exoskeleton Model, Paul Manns, Manish Sreenivasa, Matthew Millard, Katja Mombaur
Motion Optimization And Parameter Identification For A Human And Lower Back Exoskeleton Model, Paul Manns, Manish Sreenivasa, Matthew Millard, Katja Mombaur
Faculty of Engineering and Information Sciences - Papers: Part B
Designing an exoskeleton to reduce the risk of low-back injury during lifting is challenging. Computational models of the human-robot system coupled with predictive movement simulations can help to simplify this design process. Here, we present a study that models the interaction between a human model actuated by muscles and a lower back exoskeleton. We provide a computational framework for identifying the spring parameters of the exoskeleton using an optimal control approach and forward-dynamics simulations. This is applied to generate dynamically consistent bending and lifting movements in the sagittal plane. Our computations are able to predict motions and forces of the …
Patients' Acceptance Of Smartphone Health Technology For Chronic Disease Management: A Theoretical Model And Empirical Test, Kaili Dou, Ping Yu, Ning Deng, Fang Liu, Yingping Guan, Zhenye Li, Yumeng Ji, Ningkai Du, Xudong Lu, Huilong Duan
Patients' Acceptance Of Smartphone Health Technology For Chronic Disease Management: A Theoretical Model And Empirical Test, Kaili Dou, Ping Yu, Ning Deng, Fang Liu, Yingping Guan, Zhenye Li, Yumeng Ji, Ningkai Du, Xudong Lu, Huilong Duan
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Modeling And Identification Of A Realistic Spiking Neural Network And Musculoskeletal Model Of The Human Arm, And An Application To The Stretch Reflex, Manish Sreenivasa, Ko Ayusawa, Yoshihiko Nakamura
Modeling And Identification Of A Realistic Spiking Neural Network And Musculoskeletal Model Of The Human Arm, And An Application To The Stretch Reflex, Manish Sreenivasa, Ko Ayusawa, Yoshihiko Nakamura
Faculty of Engineering and Information Sciences - Papers: Part B
This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. …
Coupling A Thermal Comfort Model With Building Simulation For User Comfort And Energy Efficiency, Katharina Boudier, Massimo Fiorentini, Sabine Hoffmann, Raghuram Kalyanam, Georgios Kokogiannakis
Coupling A Thermal Comfort Model With Building Simulation For User Comfort And Energy Efficiency, Katharina Boudier, Massimo Fiorentini, Sabine Hoffmann, Raghuram Kalyanam, Georgios Kokogiannakis
Faculty of Engineering and Information Sciences - Papers: Part B
This paper describes a methodology for coupling an advanced model of the thermo-regulatory system of the human body that describes its physiological processes, a comfort model that evaluates thermal sensation and comfort, and the ESP-r building simulation software that computes the transient thermal response of a building model. The objective of this study was to utilise the physiology and comfort models to dynamically modify the heating and cooling temperature set points of a zone controller in ESP-r, in accordance with the computed human thermal sensation and achieve realtime thermal comfort management. The comunication between the software is managed by the …