Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive pr...Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive prices make it difficult to meet clinical needs.This study presents a regeneration system aimed at overcoming the challenge of inadequate supply in F.cirrhosa,focusing on:(1)callus induction,(2)bulblets and adventitious bud induction,and(3)artificial seed production.Callus development was achieved in 84.93%on Murashige and Skoog(MS)medium fortified with 1.0 mg·L^(-1) picloram.The optimal medium for callus differentiation into regenerated bulb-lets was MS medium supplemented with 1.0 mg·L^(-1)6-benzyladenine(6-BA)and 0.2 mg·L^(-1)α-naphthaleneacetic acid(NAA).Subsequently,bulblets and adventitious buds were induced from regenerated bulblet sections cul-tured on MS medium fortified with 0.3 mg·L^(-1)6-BA+1.0 mg·L^(-1)2,4-dichlorophenoxyacetic acid(2,4-D),2.0 mg·L^(-1)6-BA+0.5 mg·L^(-1),and the induction rates were 88.17%and 84.24%,respectively.The regenerated bulblets were transplanted into a substrate of humus soil,river sand,and pearlite(1:1:1)after low-temperature treatment.The germination rate was 42.80%after culture for 30 days.Regenerated bulblets were used for encap-sulations in liquid MS medium containing 3%sucrose(w/v)+0.5 mg·L^(-1) NAA+2.0 mg·L^(-1)6-BA+3%sodium alginate(w/v)with a 10 min exposure to 2%CaCl_(2).Under non-aseptic conditions,the germination rate reached 81.67%,while the rooting rate was 20.56%after 45 days.The capsule added 1.0 g·L^(-1) carbendazim and 1.0 g·L^(-1) activated carbon was the best component of artificial seeds.This study successfully established an efficient regen-eration system for the rapid propagation of F.cirrhosa,involving in vitro bulblet regeneration and artificial seed production.This method introduces a novel approach for efficient breeding and germplasm preservation,making it suitable for large-scale industrial resource production.展开更多
Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also i...Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.展开更多
A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating condition...A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.展开更多
In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results i...In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.展开更多
In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constrai...In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.展开更多
1故障现象Siemens Artis Zee系列数字减影血管造影系统(digital subtraction angiography,DSA)在使用过程中,不能进行摄影采集,只能进行X线透视。同时,检查室内参考图像显示屏底侧区域的信息栏显示“Bypass”,控制室内实时图像显示屏右...1故障现象Siemens Artis Zee系列数字减影血管造影系统(digital subtraction angiography,DSA)在使用过程中,不能进行摄影采集,只能进行X线透视。同时,检查室内参考图像显示屏底侧区域的信息栏显示“Bypass”,控制室内实时图像显示屏右侧区域的各个曝光参数变灰,且不能被修改。展开更多
Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help automate the debugging process have...Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help automate the debugging process have become a hot topic in the field of software engineering. Given the great demand for software fault localization, an approach based on the artificial bee colony （ABC） algorithm is proposed to be integrated with other related techniques. In this process, the source program is initially instrumented after analyzing the dependence information. The test case sets are then compiled and run on the instrumented program, and execution results are input to the ABC algorithm. The algorithm can determine the largest fitness value and best food source by calculating the average fitness of the employed bees in the iteralive process. The program unit with the highest suspicion score corresponding to the best test case set is regarded as the final fault localization. Experiments are conducted with the TCAS program in the Siemens suite. Results demonstrate that the proposed fault localization method is effective and efficient. The ABC algorithm can efficiently avoid the local optimum, and ensure the validity of the fault location to a larger extent.展开更多
基金funded by the National Key Research and Development Program of China(2018YFC1706101)the Science and Technology Program of Sichuan Province,China(2021YFS0045).
文摘Fritillaria cirrhosa D.Don(Liliaceae)is an endangered perennial bulbous plant and its dry bulb is a valuable med-icinal material with antitussive and expectorant effects.Nevertheless,lack of resources and expensive prices make it difficult to meet clinical needs.This study presents a regeneration system aimed at overcoming the challenge of inadequate supply in F.cirrhosa,focusing on:(1)callus induction,(2)bulblets and adventitious bud induction,and(3)artificial seed production.Callus development was achieved in 84.93%on Murashige and Skoog(MS)medium fortified with 1.0 mg·L^(-1) picloram.The optimal medium for callus differentiation into regenerated bulb-lets was MS medium supplemented with 1.0 mg·L^(-1)6-benzyladenine(6-BA)and 0.2 mg·L^(-1)α-naphthaleneacetic acid(NAA).Subsequently,bulblets and adventitious buds were induced from regenerated bulblet sections cul-tured on MS medium fortified with 0.3 mg·L^(-1)6-BA+1.0 mg·L^(-1)2,4-dichlorophenoxyacetic acid(2,4-D),2.0 mg·L^(-1)6-BA+0.5 mg·L^(-1),and the induction rates were 88.17%and 84.24%,respectively.The regenerated bulblets were transplanted into a substrate of humus soil,river sand,and pearlite(1:1:1)after low-temperature treatment.The germination rate was 42.80%after culture for 30 days.Regenerated bulblets were used for encap-sulations in liquid MS medium containing 3%sucrose(w/v)+0.5 mg·L^(-1) NAA+2.0 mg·L^(-1)6-BA+3%sodium alginate(w/v)with a 10 min exposure to 2%CaCl_(2).Under non-aseptic conditions,the germination rate reached 81.67%,while the rooting rate was 20.56%after 45 days.The capsule added 1.0 g·L^(-1) carbendazim and 1.0 g·L^(-1) activated carbon was the best component of artificial seeds.This study successfully established an efficient regen-eration system for the rapid propagation of F.cirrhosa,involving in vitro bulblet regeneration and artificial seed production.This method introduces a novel approach for efficient breeding and germplasm preservation,making it suitable for large-scale industrial resource production.
文摘Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.
基金funding from the National Natural Science Foundation of China,China(12172104,52102226)the Shenzhen Science and Technology Innovation Commission,China(JCYJ20200109113439837)the Stable Supporting Fund of Shenzhen,China(GXWD2020123015542700320200728114835006)。
文摘A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.
基金This work was supported by the Technology development Program of MSS[No.S3033853].
文摘In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.
基金Ministry of Education in Saudi Arabia for funding this research work through the project number (IFPSAU-2021/01/17793)rch work through the project number (IFPSAU-2021/01/17793)。
文摘In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.
文摘1故障现象Siemens Artis Zee系列数字减影血管造影系统(digital subtraction angiography,DSA)在使用过程中,不能进行摄影采集,只能进行X线透视。同时,检查室内参考图像显示屏底侧区域的信息栏显示“Bypass”,控制室内实时图像显示屏右侧区域的各个曝光参数变灰,且不能被修改。
文摘Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help automate the debugging process have become a hot topic in the field of software engineering. Given the great demand for software fault localization, an approach based on the artificial bee colony （ABC） algorithm is proposed to be integrated with other related techniques. In this process, the source program is initially instrumented after analyzing the dependence information. The test case sets are then compiled and run on the instrumented program, and execution results are input to the ABC algorithm. The algorithm can determine the largest fitness value and best food source by calculating the average fitness of the employed bees in the iteralive process. The program unit with the highest suspicion score corresponding to the best test case set is regarded as the final fault localization. Experiments are conducted with the TCAS program in the Siemens suite. Results demonstrate that the proposed fault localization method is effective and efficient. The ABC algorithm can efficiently avoid the local optimum, and ensure the validity of the fault location to a larger extent.
