Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include s...Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include storing and accessing user data in commercial clouds, mining of social data, and analysis of large-scale simulations and experiments such as the Large Hadron Collider. An increasing number of such data—intensive applications and services are relying on clouds in order to process and manage the enormous amounts of data required for continuous operation. It can be difficult to decide which of the many options for cloud processing is suitable for a given application;the aim of this paper is therefore to provide an interested user with an overview of the most important concepts of cloud computing as it relates to processing of Big Data.展开更多
Agroforestry can leverage the co-benefits of climate change adaptation and mitigation while conserving biodiversity and restoring degraded and deforested lands.The preference of relevant stakeholders regarding agrofor...Agroforestry can leverage the co-benefits of climate change adaptation and mitigation while conserving biodiversity and restoring degraded and deforested lands.The preference of relevant stakeholders regarding agroforestry practices enhances sustainable land management through strategic decision-making in Seychelles and other island states.A suitable approach for assessing stakeholders'preferences of agroforestry is the implementation of the strengths,weaknesses,opportunities,and threats(SWOT)approach in combination with the analytic hierarchy process(AHP)method.The entry point of this study is an extensive literature review process,during which 28 SWOT factors were identified.These SWOT factors were deliberated on during a half-day workshop with agricultural experts who agreed on 20 SWOT factors that reflect the local realities of the Seychelles through a consensus approach.Using the SWOT-AHP approach,focus group discussions were conducted to examine the perceptions of researchers and extension workers about the adoption of agroforestry in Seychelles.The results indicated that the positive aspects of smallholder agroforestry outweigh the negative aspects.For example,increased agricultural production,control runoff and soil erosion receive the highest scores among the strength factors perceived by researchers and extension workers,respectively.The willingness of international organizations to fund agroforestry-related projects and the existence of native tree species on farmlands have the highest scores among the opportunity factors.The lack of education,information,and communication between the government and farmers,and the small land size and crop competition have the highest scores among the weakness factors.Lastly,change in government policies on land use has the highest score among the threat factors by researchers,whereas the most significant threat is climate change and variability for the extension workers.The provision for a 30-year land lease agreement in the National Agroforestry Policy of Seychelles is viewed by both groups as an incentive that could potentially drive the adoption and acceptability of agroforestry.Furthermore,better coordination of various efforts to promote agroforestry and more substantial extension services for farmers,especially the role of technologies for optimal production on small plots of land,can enhance climate resilience in Seychelles and other small island developing states.展开更多
Future vehicular Internet-of-Things(IoT)systems feature a large number of devices and multi-access environments where different types of communication,computing,and storage resources must be efficiently utilized.At th...Future vehicular Internet-of-Things(IoT)systems feature a large number of devices and multi-access environments where different types of communication,computing,and storage resources must be efficiently utilized.At the same time,novel services such as cooperative autonomous driving and intelligent transportation systems(ITS),that demand unprecedented high accuracy,ultra-low latency,and large bandwidth,are emerging.展开更多
Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being purs...Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.展开更多
The FORTRAN programming language was used in early days of writing finite element field computation to write programs Much of those codes were developed in an ad hoc way. Today, modem software developers face problems...The FORTRAN programming language was used in early days of writing finite element field computation to write programs Much of those codes were developed in an ad hoc way. Today, modem software developers face problems in understanding, modifying and utilizing those codes. As modem software engineers are very concerned with object oriented design, if those codes are converted into an object oriented language, they could be redesigned and deployed in an object oriented system. Those legacy codes often need to be converted not only into an object oriented programming language such as Java but also into functional oriented languages such as C. Conversion of those legacy codes into modem languages gives many advantages. The purpose of this paper is to compare the performances of such converted legacy finite element codes originally written in FORTRAN, the relevant converted C program and Java program. Sample finite element programs written in FORTRAN are converted for purposes of comparison into modem languages such as C and Java. Performances are compared based on the execution time. In addition to that, the memory sizes of the execution file of FORTRAN and C programs are also compared. Java being interpretive there is no execution file to compare.展开更多
Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine l...Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper proposes an online multi-kernel extreme learning machine （OM-ELM） which assembles multiple ELM classifiers and optimizes the kernel weights with a p-norm formulation of multi-kernel learning （MKL） problem. It can be applied in feature fusion applications that require incremental learning over multiple sequential sensory readings. The performance of OM-ELM is tested towards four different robot recognition tasks. By comparing to several state-of-the-art online models for multi-kernel learning, we claim that our method achieves a superior or equivalent training accuracy and generalization ability with less training time. Practical suggestions are also given to aid effective online fusion of robot sensory features.展开更多
The cytokine granulocyte-macrophage-colony stimulating factor (GM-CSF) possesses the capacity to differentiate monocytes into macrophages (MØs) with opposing functions, namely, proinflammatory M1-like MØs an...The cytokine granulocyte-macrophage-colony stimulating factor (GM-CSF) possesses the capacity to differentiate monocytes into macrophages (MØs) with opposing functions, namely, proinflammatory M1-like MØs and immunosuppressive M2-like MØs. Despite the importance of these opposing biological outcomes, the intrinsic mechanism that regulates the functional polarization of MØs under GM-CSF signaling remains elusive. Here, we showed that GM-CSF-induced MØ polarization resulted in the expression of cytokine-inducible SH2-containing protein (CIS) and that CIS deficiency skewed the differentiation of monocytes toward immunosuppressive M2-like MØs. CIS deficiency resulted in hyperactivation of the JAK-STAT5 signaling pathway, consequently promoting downregulation of the transcription factor Interferon Regulatory Factor 8 (IRF8). Loss- and gain-of-function approaches highlighted IRF8 as a critical regulator of the M1-like polarization program. In vivo, CIS deficiency induced the differentiation of M2-like macrophages, which promoted strong Th2 immune responses characterized by the development of severe experimental asthma. Collectively, our results reveal a CIS-modulated mechanism that clarifies the opposing actions of GM-CSF in MØ differentiation and uncovers the role of GM-CSF in controlling allergic inflammation.展开更多
This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)approach.The proposed MADQN is appli...This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)approach.The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly rainfall.MADQN is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)approaches.MADQN enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia.Investigation is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic network.Our proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.展开更多
Trip recommendation has become increasingly popular with the rapid growth of check-in data in location-based social networks.Most existing studies focused only on the popularity of trips.In this paper,we consider furt...Trip recommendation has become increasingly popular with the rapid growth of check-in data in location-based social networks.Most existing studies focused only on the popularity of trips.In this paper,we consider further the usability of trip recommendation results through spatial diversification.We thereby formulate a new type of queries named spatial diversified top-κroutes(SDκR)query.This type of queries finds k trip routes with the highest popularity,each of which starts at a given starting point,consumes travel time within a given time budget,and passes through points of interest(POIs)of given categories.Any two trip routes returned are diversified to a certain degree defined by the spatial distance between the two routes.We show that the SDkR problem is NP-hard.We propose two precise algorithms to solve the problem.The first algorithm starts with identifying all candidate routes that satisfy the query constraints,and then searches for theκ-route combination with the highest popularity.The second algorithm identifies the candidate routes and builds up the optimalκ-route combination progressively at the same time.Further,we propose an approximate algorithm to obtain even higher query efficiency with precision bounds.We demonstrate the effectiveness and efficiency of the proposed algorithms on real datasets.Our experimental results show that our algorithms find popular routes with diversified POI locations.Our approximate algorithm saves up to 90%of query time compared with the baseline algorithms.展开更多
文摘Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include storing and accessing user data in commercial clouds, mining of social data, and analysis of large-scale simulations and experiments such as the Large Hadron Collider. An increasing number of such data—intensive applications and services are relying on clouds in order to process and manage the enormous amounts of data required for continuous operation. It can be difficult to decide which of the many options for cloud processing is suitable for a given application;the aim of this paper is therefore to provide an interested user with an overview of the most important concepts of cloud computing as it relates to processing of Big Data.
基金The United Nations Development Programme(UNDP)Small Grants Program supported this work through the project“Exploring Innovative Opportunities for Promoting Synergies between Climate Change Adaptation and Mitigation in Seychelles”(SEY/SGP/OP6/Y5/CORE/YCC/2019/25),under the youth and climate change portfolio implemented by the University of Seychelles。
文摘Agroforestry can leverage the co-benefits of climate change adaptation and mitigation while conserving biodiversity and restoring degraded and deforested lands.The preference of relevant stakeholders regarding agroforestry practices enhances sustainable land management through strategic decision-making in Seychelles and other island states.A suitable approach for assessing stakeholders'preferences of agroforestry is the implementation of the strengths,weaknesses,opportunities,and threats(SWOT)approach in combination with the analytic hierarchy process(AHP)method.The entry point of this study is an extensive literature review process,during which 28 SWOT factors were identified.These SWOT factors were deliberated on during a half-day workshop with agricultural experts who agreed on 20 SWOT factors that reflect the local realities of the Seychelles through a consensus approach.Using the SWOT-AHP approach,focus group discussions were conducted to examine the perceptions of researchers and extension workers about the adoption of agroforestry in Seychelles.The results indicated that the positive aspects of smallholder agroforestry outweigh the negative aspects.For example,increased agricultural production,control runoff and soil erosion receive the highest scores among the strength factors perceived by researchers and extension workers,respectively.The willingness of international organizations to fund agroforestry-related projects and the existence of native tree species on farmlands have the highest scores among the opportunity factors.The lack of education,information,and communication between the government and farmers,and the small land size and crop competition have the highest scores among the weakness factors.Lastly,change in government policies on land use has the highest score among the threat factors by researchers,whereas the most significant threat is climate change and variability for the extension workers.The provision for a 30-year land lease agreement in the National Agroforestry Policy of Seychelles is viewed by both groups as an incentive that could potentially drive the adoption and acceptability of agroforestry.Furthermore,better coordination of various efforts to promote agroforestry and more substantial extension services for farmers,especially the role of technologies for optimal production on small plots of land,can enhance climate resilience in Seychelles and other small island developing states.
文摘Future vehicular Internet-of-Things(IoT)systems feature a large number of devices and multi-access environments where different types of communication,computing,and storage resources must be efficiently utilized.At the same time,novel services such as cooperative autonomous driving and intelligent transportation systems(ITS),that demand unprecedented high accuracy,ultra-low latency,and large bandwidth,are emerging.
文摘Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.
文摘The FORTRAN programming language was used in early days of writing finite element field computation to write programs Much of those codes were developed in an ad hoc way. Today, modem software developers face problems in understanding, modifying and utilizing those codes. As modem software engineers are very concerned with object oriented design, if those codes are converted into an object oriented language, they could be redesigned and deployed in an object oriented system. Those legacy codes often need to be converted not only into an object oriented programming language such as Java but also into functional oriented languages such as C. Conversion of those legacy codes into modem languages gives many advantages. The purpose of this paper is to compare the performances of such converted legacy finite element codes originally written in FORTRAN, the relevant converted C program and Java program. Sample finite element programs written in FORTRAN are converted for purposes of comparison into modem languages such as C and Java. Performances are compared based on the execution time. In addition to that, the memory sizes of the execution file of FORTRAN and C programs are also compared. Java being interpretive there is no execution file to compare.
文摘Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper proposes an online multi-kernel extreme learning machine （OM-ELM） which assembles multiple ELM classifiers and optimizes the kernel weights with a p-norm formulation of multi-kernel learning （MKL） problem. It can be applied in feature fusion applications that require incremental learning over multiple sequential sensory readings. The performance of OM-ELM is tested towards four different robot recognition tasks. By comparing to several state-of-the-art online models for multi-kernel learning, we claim that our method achieves a superior or equivalent training accuracy and generalization ability with less training time. Practical suggestions are also given to aid effective online fusion of robot sensory features.
基金supported by National Health and Medical Research Council of Australia(NHMRC)grants(1037321,1105209,1143976,1150425,1080321,1196335,5575500,1054925,and 1048278)an NHMRC Independent Research Institutes Infrastructure Support Scheme grant(361646)a Victorian State Government Operational Infrastructure Support grant.JB was supported by the Stafford Fox Medical Research Foundation.
文摘The cytokine granulocyte-macrophage-colony stimulating factor (GM-CSF) possesses the capacity to differentiate monocytes into macrophages (MØs) with opposing functions, namely, proinflammatory M1-like MØs and immunosuppressive M2-like MØs. Despite the importance of these opposing biological outcomes, the intrinsic mechanism that regulates the functional polarization of MØs under GM-CSF signaling remains elusive. Here, we showed that GM-CSF-induced MØ polarization resulted in the expression of cytokine-inducible SH2-containing protein (CIS) and that CIS deficiency skewed the differentiation of monocytes toward immunosuppressive M2-like MØs. CIS deficiency resulted in hyperactivation of the JAK-STAT5 signaling pathway, consequently promoting downregulation of the transcription factor Interferon Regulatory Factor 8 (IRF8). Loss- and gain-of-function approaches highlighted IRF8 as a critical regulator of the M1-like polarization program. In vivo, CIS deficiency induced the differentiation of M2-like macrophages, which promoted strong Th2 immune responses characterized by the development of severe experimental asthma. Collectively, our results reveal a CIS-modulated mechanism that clarifies the opposing actions of GM-CSF in MØ differentiation and uncovers the role of GM-CSF in controlling allergic inflammation.
文摘This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)approach.The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly rainfall.MADQN is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)approaches.MADQN enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia.Investigation is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic network.Our proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.
基金the National Key Research and Development Program of China under Grant No.2018YFB1003404the National Natural Science Foundation of China under Grant Nos.61872070,U1435216,U1811261,and 61602103the Fundamental Research Funds for the Central Universities of China under Grant No.N171605001.
文摘Trip recommendation has become increasingly popular with the rapid growth of check-in data in location-based social networks.Most existing studies focused only on the popularity of trips.In this paper,we consider further the usability of trip recommendation results through spatial diversification.We thereby formulate a new type of queries named spatial diversified top-κroutes(SDκR)query.This type of queries finds k trip routes with the highest popularity,each of which starts at a given starting point,consumes travel time within a given time budget,and passes through points of interest(POIs)of given categories.Any two trip routes returned are diversified to a certain degree defined by the spatial distance between the two routes.We show that the SDkR problem is NP-hard.We propose two precise algorithms to solve the problem.The first algorithm starts with identifying all candidate routes that satisfy the query constraints,and then searches for theκ-route combination with the highest popularity.The second algorithm identifies the candidate routes and builds up the optimalκ-route combination progressively at the same time.Further,we propose an approximate algorithm to obtain even higher query efficiency with precision bounds.We demonstrate the effectiveness and efficiency of the proposed algorithms on real datasets.Our experimental results show that our algorithms find popular routes with diversified POI locations.Our approximate algorithm saves up to 90%of query time compared with the baseline algorithms.
