A multi-scale modelling framework to guide management of plant invasions in a transboundary context

Background：Attention has recently been drawn to the issue of transboundary invasions,where species introduced and naturalized in one country cross international borders and become problematic in neighbouring countries.Robust modelling frameworks,able to identify the environmental drivers of invasion and forecast the current and future potential distribution of invasive species,are needed to study and manage invasions.Limitations due to the lack of species distribution and environmental data,or assumptions of modelling tools,often constrain the reliability of model predictions.Methods：We present a multiscale spatial modelling framework for transboundary invasions,incorporating robust modelling frameworks（Multimodel Inference and Ensemble Modelling） to overcome some of the limitations.The framework is illustrated using Hakea sericea Schrad.（Proteaceae）,a shrub or small tree native to Australia and invasive in several regions of the world,including the Iberian Peninsula.Two study scales were considered：regional scale（western Iberia,including mainland Portugal and Galicia） and local scale（northwest Portugal）.At the regional scale,the relative importance of environmental predictors sets was evaluated and ranked to determine the main general drivers for the species distribution,while the importance of each environmental predictor was assessed at the local scale.The potential distribution of H.sericea was spatially projected for both scale areas.Results：Model projections for western Iberia suggest that a large area is environmentally suitable in both Portugal and Spain.Climate and landscape composition sets were the most important determinants of this regional distribution of the species.Conversely,a geological predictor（schist lithology） was more important in explaining its local-scale distribution.Conclusions：After being introduced to Portugal,H.sericea has become a transboundary invader by expanding in parts of Galicia（Spain）.The fact that a larger area is predicted as environmentally suitable in Spain raises concerns regarding its potential continued expansion.This highlights the importance of transboundary cooperation in the early management of invasions.By reliably identifying drivers and providing spatial projections of invasion at multiple scales,this framework provides insights for the study and management of biological invasions,including the assessment of transboundary invasion risk.

Stochastic Variational Inference-Based Parallel and Online Supervised Topic Model for Large-Scale Text Processing

Topic modeling is a mainstream and effective technology to deal with text data, with wide applications in text analysis, natural language, personalized recommendation, computer vision, etc. Among all the known topic models, supervised Latent Dirichlet Allocation （sLDA） is acknowledged as a popular and competitive supervised topic model. How- ever, the gradual increase of the scale of datasets makes sLDA more and more inefficient and time-consuming, and limits its applications in a very narrow range. To solve it, a parallel online sLDA, named PO-sLDA （Parallel and Online sLDA）, is proposed in this study. It uses the stochastic variational inference as the learning method to make the training procedure more rapid and efficient, and a parallel computing mechanism implemented via the MapReduce framework is proposed to promote the capacity of cloud computing and big data processing. The online training capacity supported by PO-sLDA expands the application scope of this approach, making it instrumental for real-life applications with high real-time demand. The validation using two datasets with different sizes shows that the proposed approach has the comparative accuracy as the sLDA and can efficiently accelerate the training procedure. Moreover, its good convergence and online training capacity make it lucrative for the large-scale text data analyzing and processing.

Structural Reliability Analysis Method in Fuzzy Environment

In this paper, the fuzzy-set-based structural possibility theory is investigated, and this theory can be used to deal with the subjective uncertainties in the design of engineering structures. Furthermore, a comprehensive model of structural safety assessment, which can merge subjective uncertainties with objective uncertainties, is presented. In this model, the fuzziness of stress-strength inference model, safety margin functions of single or multiple limit-state, structural failure state and the final assessment result are taken into account. This continuous model can be transformed into an equivalent model of probability-based and solved by the present structural reliability analysis method and parallel algorithm. An example is given to show the main idea of the method presented in this paper.

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

Accurate intelligent reasoning systems are vital for intelligent manufacturing.In this study,a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters.The developed system consists of a self-learning algorithm with an improved particle swarm optimization(IPSO)learning algorithm,prediction model determined by an improved case-based reasoning(ICBR)method,and optimization model containing an improved adaptive neural fuzzy inference system(IANFIS)and IPSO.Experimental results showed that the IPSO algorithm exhibited the best global convergence performance.The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods.The IANFIS model,in combination with IPSO,enabled the optimization of multiple objectives,thus generating optimal milling parameters.This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.

Inference after covariate-adaptive randomisation:aspects of methodology and theory

Covariate-adaptive randomisation has a more than 45 years of history of applications in clinical trials,in order to balance treatment assignments across prognostic factors that may have influence on the outcomes of interest.However,almost no theory had been developed for covariate-adaptive randomisation until a paper on the theory of testing hypotheses published in 2010.In this article,we review aspects of methodology and theory developed in the last decade for statistical inference under covariate-adaptive randomisation.Wefocus on issues such as whether a conventional procedure valid under the assumption that treatments are assigned completely at random is still valid or conservative when the actual randomisation is covariateadaptive,how a valid inference procedure can be obtained by modifying a conventional method or directly constructed by stratifying the covariates used in randomisation,whether inference procedures have different properties when covariate-adaptive randomisation schemes have different degrees of balancing assignments,and how to further adjust covariates in the inference procedures to gain more efficiency.Recommendations are made during the review and further research problems are discussed.

Model learning:a survey of foundations,tools and applications

Software systems are present all around us and playing their vital roles in our daily life.The correct functioning of these systems is of prime concern.In addition to classical testing techniques,formal techniques like model checking are used to reinforce the quality and reliability of software systems.However,obtaining of behavior model,which is essential for model-based techniques,of unknown software systems is a challenging task.To mitigate this problem,an emerging black-box analysis technique,called Model Learning,can be applied.It complements existing model-based testing and verification approaches by providing behavior models of blackbox systems fully automatically.This paper surveys the model learning technique,which recently has attracted much attention from researchers,especially from the domains of testing and verification.First,we review the background and foundations of model learning,which form the basis of subsequent sections.Second,we present some well-known model learning tools and provide their merits and shortcomings in the form of a comparison table.Third,we describe the successful applications of model learning in multidisciplinary fields,current challenges along with possible future works,and concluding remarks.

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface quality.However,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments.Herein,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface quality.In the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership inferior.The membership functions are varied based on the machining theory.The inputs of the NANFIS model are cutting parameters,and the outputs are the machining performances.For optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS models.Additionally,the IPSO algorithm as a learning algorithm is used to train the NANFIS models.The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron.The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,respectively.The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models.Based on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency.It is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

The Proportional Hazards Model for Multiple Type Recurrent Gap Times

Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies,and often more than one type of recurrent events is of interest.In this paper,we consider a proportional hazards model for multiple type recurrent gap times data to assess the effect of covaxiates on the censored event processes of interest.An estimating equation approach is used to obtain the estimators of regression coefficients and baseline cumulative hazard functions.We examine asymptotic properties of the proposed estimators.Finite sample properties of these estimators are demonstrated by simulations.

Challenges and Perspectives in Target Identification and Mechanism Illustration for Chinese Medicine

Chinese medicine(CM)is an important resource for human life understanding and discovery of drugs.However,due to the unclear pharmacological mechanism caused by unclear target,research and international promotion of many active components have made little progress in the past decades of years.CM is mainly composed of multi-ingredients with multi-targets.The identification of targets of multiple active components and the weight analysis of multiple targets in a specific pathological environment,that is,the determination of the most important target is the main obstacle to the mechanism clarification and thus hinders its internationalization.In this review,the main approach to target identification and network pharmacology were summarized.And BIBm(Bayesian inference modeling),a powerful method for drug target identification and key pathway determination was introduced.We aim to provide a new scientific basis and ideas for the development and international promotion of new drugs based on CM.

Development of methods for establishing nutrient criteria in lakes and reservoirs: A review

Nutrient criteria provide a scientific foundation for the comprehensive evaluation, prevention,control and management of water eutrophication. In this review, the literature was examined to systematically evaluate the benefits, drawbacks, and applications of statistical analysis,paleolimnological reconstruction, stressor-response model, and model inference approaches for nutrient criteria determination. The developments and challenges in the determination of nutrient criteria in lakes and reservoirs are presented. Reference lakes can reflect the original states of lakes, but reference sites are often unavailable. Using the paleolimnological reconstruction method, it is often difficult to reconstruct the historical nutrient conditions of shallow lakes in which the sediments are easily disturbed. The model inference approach requires sufficient data to identify the appropriate equations and characterize a waterbody or group of waterbodies, thereby increasing the difficulty of establishing nutrient criteria. The stressor-response model is a potential development direction for nutrient criteria determination, and the mechanisms of stressor-response models should be studied further. Based on studies of the relationships among water ecological criteria, eutrophication, nutrient criteria and plankton, methods for determining nutrient criteria should be closely integrated with water management requirements.

Research on the Method of Shipping Risk Early-Warning Based on Matter-Element Theory

The purpose of shipping risk early-warning is that some effective measures are taken to reduce risk probability before the risk brings heavy loss.The shipping risk has the dynamic characteristic,so the key of early-warning is to choice risk early-warning index correctly and evaluate risk grade quantitatively.According to the element extension theory,the rhombus inference model is applied to establish the index system.And the problem of risk grade evaluation can be solved by the assessment model of multi-index performance parameter,which is developed by the extension engineering method.Finally,the main shipping risks and their grades are identified by the example analysis based on the statistical data,which shows the effective and feasible of the shipping risk early-warning method.

Measuring County Resilience After the 2008 Wenchuan Earthquake

The catastrophic earthquake that struck Sichuan Province,China,in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern China.In recent years,great attention has been paid to the resilience of the affected area.This study applied the resilience inference measurement(RIM) model to quantify and validate the community resilience of 105 counties in the impacted area.The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure,damage,and recovery conditions.The model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county's resilience.Analysis results show that counties located at the epicenter had the lowest resilience,but counties immediately adjacent to the epicenter had the highest resilience capacities.Counties that were farther away from the epicenter returned to normal resiliency quickly.Socioeconomic variables—including sex ratio,per capita GDP,percent of ethnic minority,and medical facilities—were identified as the most influential characteristics influencing resilience.This study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.