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Detectability of the global weed Hypochaeris radicata is influenced by species, environment and observer characteristics

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Journal of Plant Ecology 2015年第4期8卷 449-455页

作者： Katherina Ng Don A.Driscoll arc centre of excellence for environmental decisions The NERP Environmental Decisions HubFenner School of Environment and SocietyThe Australian National UniversityLinnaeus WayCanberraACT 2601Australia

Aims to determine the detectability of a global weedy perennial weed Hypochaeris radicata and its relationship with five common observer,species and environmental variables.Methods trained independent observers conducted time-limited repeat sur-veys of H.radicata during autumn in an endangered grassy box-gum woodland ecosystem in south-east australia.single-species single-season site-occupancy modelling was used to determine if detectability of H.radicata was altered by five covariates,observer,litter height,grazing,maximum plant height and flowering state.Important Findings Detectability for H.radicata varied significantly with observer,litter height,plant maximum height and flowering state,but not with graz-ing.Despite significant observer-specific variation,there was a con-sistent increase in detectability with plant height and when plants are in flower for all observers.Detectability generally decreased as litter height increases.Perfect or constant detection rates cannot be assumed in plant surveys,even for easily recognizable plants in simple survey conditions.understanding how detectability is influ-enced by common survey variables can help improve the efficacy of plant monitoring programs by quantifying the extent of uncertainty in inferences made from survey data,or by determining optimal sur-vey conditions to increase the reliability of collected data.For plants with traits similar to H.radicata,surveying when most plants are at maximum height or in flower,increasing search intensity when litter levels are high and minimizing observer-related heterogeneity are potentially simple and effective ways to reduce detection errors.We speculate that detection rates may be lower,more variable and involve additional covariates when surveying during the peak flow-ering spring season with the presence of more warm season and taller annual species.

关键词： Hypochaeris radicata grassy woodland detection probability detectability vegetation survey plant monitoring observation bias false absence

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Growth's secret maestros:LBD11-ROS harmony drives vascular cambium activity in Arabidopsis

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Molecular Plant 2023年第8期16卷 1246-1248页

作者： Melis Kucukoglu Topcu Rishikesh P.Bhalerao Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental SciencesVikki Plant Science CentreUniversity of Helsinki00014 HelsinkiFinland Umea Plant Science centre Department of Forest Genetics and Plant PhysiologySwedish University of Agricultural Sciences90187 UmeaSweden

Integration of metabolic products such as reactive oxygen species(ROS)into vital processes play essential roles in plants.ROS refers to oxygen-derived free radicals,which exhibit a higher reactivity compared to the diatomic oxygen molecule(O2)(Waszczak et al.,2018).Numerous forms of ROS have been identified in plants with various degrees of stability:singlet oxygen(1O2),superoxide anion(O2·−),hydrogen peroxide(H2O2),and hydroxyl radical(HO·)are the major forms.ROS are generated during normal plant growth as products of aerobic metabolism in almost all cellular compartments,including chloroplasts,mitochondria,and peroxisomes as well as in apoplast.However,elevated ROS levels are detrimental to plant cells,posing a substantial risk of DNA damage and improper initiation of programmed cell death.Thus,the levels of ROS are meticulously balanced through intricate antioxidant systems,which tightly control their generation and breakdown(Waszczak et al.,2018;Huang et al.,2019).

关键词： reactivity singlet stability

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Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification,case study:Fars domain,Iran

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Geo-Spatial Information Science 2022年第3期25卷 399-412页

作者： Sahand Seraj Mahmoud Reza Delavar School of Surveying and Geospatial Engineering University of TehranTehranIran Center of excellence in Geomatics Engineering in Disaster Management University of TehranTehranIran

It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock,reservoir rock,trap and seal rock.In order to overcome such attributes with uncertainties,a number of soft computing methods are used.Information granules could be provided by the Rough Fuzzy Set Granulation(RFSG)with a thorough quality evaluation.This is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon systems.This paper is an endeavor to recommend a Geospatial Information System(GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular computing.The model used the RFSG for the attribute reduction by a Decision Logic language(DLlanguage).The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of uncertainty.In order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification,the fuzzy entropy and fuzzy cross-entropy are applied.The proposed RFSG model applied for 62 structures as the training data,average fuzzy entropy has been calculated as 0.85,whereas the average fuzzy cross-entropy has been calculated 0.18.As it can be discerned,just seven structures had cross-entropies greater than 0.1,while three structures were larger than 0.3.It is implied that the precision of the proposed model is about 89%.The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values.

关键词： Rough fuzzy set granular computing Geospatial Information Science(GIS) petroleum system hydrocarbon structure

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Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

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Computer Modeling in Engineering & Sciences 2022年第12期133卷 843-872页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Mazen Ebraheem Assiri Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Electronics&Telecommunication Vilad GhatDr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India centre for AdvancedModelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydney2007Australia Center of excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation centre Universiti KebangsaanInstitute of Climate ChangeMalaysiaSelangor43600Malaysia Department of Computer Science LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Center for Applied Artificial Intelligence(SCAAI) LavaleSymbiosis International(Deemed University)Pune412115India

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent years.Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing power.Deep learning models are at the forefront of this advancement.However,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their conclusions.Such a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep learningmodels.The situation is substantially different in safety-critical applications.The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case.Significantly,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an option.This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment industries.Explainability is the term introduced in the preceding years.The AImodel’s black box nature will become explainable with these frameworks.Especially in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial use.These models’explainable natures will help them commercially in diagnosis decisions in the medical field.This paper explores the different frameworks for the explainability of AI models in the medical field.The available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

关键词： Medical imaging explainability artificial intelligence XAI

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Reinforcement learning-driven local transactive energy market fordistributed energy resources

能源与人工智能(英文)

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能源与人工智能(英文) 2022年第2期8卷 162-176页

作者： Steven Zhang Daniel May Mustafa Gül Petr Musilek Electrical and Computer Engineering University of AlbertaCanada Civil and environmental Engineering University of AlbertaCanada

Local energy markets are emerging as a tool for coordinating generation, storage, and consumption of energyfrom distributed resources. In combination with automation, they promise to provide an effective energymanagement framework that is fair and brings system-level savings. The cooperative–competitive natureof energy markets calls for multi-agent based automation with learning energy trading agents. However,depending on the dynamics of the agent–environment interaction, this approach may yield unintended behaviorof market participants. Thus, the design of market mechanisms suitable for reinforcement learning agentsmust take into account this interplay. This article introduces autonomous local energy exchange (ALEX) asan experimental framework that combines multi-agent learning and double auction mechanism. Participantsdetermine their internal price signals and make energy management decisions through market interactions,rather than relying on predetermined external price signals. The main contribution of this article is examinationof compatibility between specific market elements and independent learning agents. Effects of different marketproperties are evaluated through simulation experiments, and the results are used for determine a suitablemarket design. The results show that market truthfulness maintains demand-response functionality, while weakbudget balancing provides a strong reinforcement signal for the learning agents. The resulting agent behavioris compared with two baselines: net billing and time-of-use rates. The ALEX-based pricing is more responsiveto fluctuations in the community net load compared to the time-of-use. The more accurate accounting ofrenewable energy usage reduced bills by a median 38.8% compared to net billing, confirming the ability tobetter facilitate demand response.

关键词： Transactive energy Demand response Distributed energy resources(DER) DER integration Local energy market Reinforcement learning

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Systems Biology Approach Pinpoints Minimum Requirements for Auxin Distribution during Fruit Opening

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Molecular Plant 2019年第6期12卷 863-878页

作者： Xin-Ran Li Renske M.A.Vroomans Samantha Fox Veronica A.Grieneisen Lars Φstergaard Athanasius F.M.Maree Crop Genetics John Innes Centre Norwich NR4 7UH UK Computational and Systems Biology John Innes Centre Norwich NR4 7UH UK centre of excellence in Computational and Experimental Developmental Biology Institute of Biotechnology University of Helsinki 00014 Helsinki Finland Cell and Developmental Biology John Innes Centre Norwich NR4 7UH UK School of Biosciences Cardiff University Cardiff CF10 3AX Wales UK

The phytohormone auxin is implied in steering various developmental decisions during plant morphogenesis in a concentration-dependent manner.Auxin maxima have been shown to maintain meristematic activity,for example,of the root apical meristem,and position new sites of outgrowth,such as during lateral root initiation and phyllotaxis.More recently,it has been demonstrated that sites of auxin minima also provide positional information.In the developingArabidopsis fruit,auxin minima are required for correct differentiation of the valve margin.It remains unclear,however,how this auxin minimum is generated and maintained.Here,we employ a systems biology approach to model auxin transport based on experimental observations.This allows us to determine the minimal requirements for its establishment.Our simulations reveal that two alternative processes-which we coin "flux-barrier" and "flux-passage"-are both able to generate an auxin minimum,but under different parameter settings.Both models are in principle able to yield similar auxin profiles but present qualitatively distinct patterns of auxin flux.The models were tested by tissue-specific inducible ablation,revealing that the auxin minimum in the fruit is most likely generated by a flux-passage process.Model predictions were further supported through 3D PIN localization imaging and implementing experimentally observed transporter localization.Through such an experimental-modeling cycle,we predict how the auxin minimum gradually matures during fruit development to ensure timely fruit opening and seed dispersal.

关键词： auxin mathematical modeling polar auxin transport fruit development systems biology of patterning

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Energy efficiency and savings analysis with multirate sampling for petrochemical process using convolutional neural network-based transfer learning

Energy and AI

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Energy and AI 2023年第4期14卷 43-59页

作者： Chanin Panjapornpon Santi Bardeeniz Mohamed Azlan Hussain Kanthika Vongvirat Chayanit Chuay-ock Department of Chemical Engineering Center of Excellence on Petrochemicals and Materials TechnologyFaculty of EngineeringKasetsart UniversityBangkok10900Thailand Department of Chemical Engineering Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia

Energy efficiency in the petrochemical industry is crucial in reducing energy consumption and environmental impact.An accurate energy efficiency model will provide valuable insight for supporting operational adjustment decisions.In practice,due to inconsistent sampling intervals in the petrochemical industry,the traditional approach for obtaining energy efficiency may be unreliable and difficult to handle these multirate data char-acteristics.Therefore,in this paper,a multi-channel convolutional neural network model integrating a model parameter-based transfer learning approach is proposed to improve the prediction of energy efficiency under inconsistent sampling intervals.The multi-channel structure aims to recognize a different pattern from the dataset by convolving the information along the time dimension.Concurrently,transfer learning allows the model to learn a new pattern of input after the model is fully trained.Finally,the performance for energy ef-ficiency prediction and saving analysis is validated by applying it to the vinyl chloride monomer production case study.The result shows that the proposed model outperformed traditional models and typical convolutional neural network structures in terms of accuracy and reproducibility,with an r-square of 0.97.The utilization of transfer learning prevents a significant drop in performance and enhances adaptability in model learning on real-time energy tracking.Moreover,the energy gap analysis of the prediction result identified a significant energysaving potential,which would decrease annual energy consumption by 7.25%on average and a 5,709-ton reduction in carbon dioxide emissions.

关键词： Energy efficiency prediction Transfer learning Petrochemical process Multirate prediction Convolutional neural network

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Call for papers for a special issue on evidence-based practice in nursing

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International Journal of Nursing Sciences 2023年第4期10卷 602-602页

作者： Hong-Lin Chen Yan Hu Nantong University China Fudan University School of Nursing ShanghaiChina Fudan University centre for Evidence-based Nursing:A Joanna Briggs Institute centre of excellence ShanghaiChina

Evidence-based practice in nursing involves the conscientious,explicit,and judicious use of current best evidence in making decisions about the nursing care of patients,which gives equal consideration to the best available evidence;the context in which the care is delivered;patient preference;and the professional judgement of the health professional.This Special Focus Issue aims to report projects and studies about evidence-based practice in nursing.

关键词： nursing Focus patients

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Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes

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Information Processing in Agriculture 2017年第1期4卷 50-63页

作者： Joaquın Canadas Jorge Antonio Sanchez-Molina Francisco Rodrıguez Isabel Marıa del Aguila Department of Informatics University of Almerı´a04120 Almerı´aSpain The Agrifood Campus of International Excellence(ceiA3)Spain

Crop growth in greenhouses is basically determined by the climate variables in the environment and by the amounts of water and fertilizers supplied by irrigation.The management of these factors depends on the expertise of agricultural technicians and farmers,usually assisted by control systems installed within the greenhouse.In this context,decision support features enable us to incorporate invaluable human experience so thatwe can take quick and effective decisions to ensure efficient crop growth.This work describes a real-time decision support system for greenhouse tomatoes that supports decisions at three stages–the supervision stage identifies climate sensor faults,the control stage maintains climate variables at setpoints,and the strategic stage identifies diseases affecting the crop and changes climate variables accordingly to minimize damage.The DSS was implemented by integrating a real-time rule-based tool into the control system.Experimental results show that the system increases climate control effectiveness,while providing support in preventing diseaseswhich are difficult to eradicate.The system was tested by simulating the appearance of the disease and observing the real systemresponse.The main contribution has been to demonstrate that production rules,which aremature and well-known in the artificial intelligence domain,can act as a shared technology for the whole system.This means that fault detection,temperature control and disease monitoring features are not dealt with in isolation.

关键词： Greenhouse crop growth Decision support systems Climate control Disease management

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Statistics as Part of Scientific Reasoning in Plant Sciences:Overlooked Issues and Recommended Solutions

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Molecular Plant 2019年第1期12卷 7-9页

作者： Ying Zhang Chi-Kuang Wen National Key Laboratory of Plant Molecular Genetics CAS Center for Excellence in Molecular Plant Sciencesinstitute of Plant Physiology and EcologyChinese Academy of SciencesShanghai 200032China

New research tools for modern life sciences are emerging every few years and being implemented to reveal the underlying mechanisms of scientific questions of interest.However,statistical practice in modern life sciences has barely moved forward and probably has regressed.In many cases,the decisions from biological experiments are predominantly made by using significant P values involving inappropriate statistical analyses and standards,and positive,favored results are preferentially reported (Fanelli,2012;Head et al.,2015;Demidenko,2016;Yong, 2017).

关键词： life research questions

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