MPI‐CHECK: a tool for checking Fortran 90 MPI programs

MPI is commonly used to write parallel programs for distributed memory parallel computers. MPI‐CHECK is a tool developed to aid in the debugging of MPI programs that are written in free or fixed format Fortran 90 and Fortran 77. MPI‐CHECK provides automatic compile‐time and run‐time checking of MPI programs. MPI‐CHECK automatically detects the following problems in the use of MPI routines: (i) mismatch in argument type, kind, rank or number; (ii) messages which exceed the bounds of the source/destination array; (iii) negative message lengths; (iv) illegal MPI calls before MPI_INIT or after MPI_FINALIZE; (v) inconsistencies between the declared type of a message and its associated DATATYPE argument; and (vi) actual arguments which violate the INTENT attribute. Copyright © 2003 John Wiley & Sons, Ltd.     

Challenges of integrated modelling in mining regions to address social,environmental and economic impacts

Planning in mining regions needs to accommodate the extraction of minerals/energy resources in co-existence with established land uses, such as agriculture and ecological conservation. Here, we first identify six critical aspects of planning in mining regions: i) the temporal nature of mining operations; ii) spatial dimensions of mining operations; iii) irreversible changes that create post-mining landscapes; iv) social dimensions of mining impacts and corporate responsibility; v) cumulative dimensions of impacts; and vi) a need to integrate methods from a range of disciplines. We then illustrate the potential to address these challenges using integrative modelling nested within a participatory approach to allow for clear, transparent, and stakeholder-inclusive decision-making. We describe a 5-step framework that supports a broadening of strategic assessments and offers mining companies forewarning about potential environmental and social conflicts. Case studies are needed to assess and refine the proposed framework and develop guidance for its use.     

Decomposing time series into deterministic and stochastic influences: A survey

Temporal data produced by industrial, human, and natural phenomena typically contain deterministic and stochastic influences, being the first ideally modelled using Dynamical Systems while the second is appropriately addressed using Statistical tools. Although such influences have been widely studied as individual components, specific tools are required to support their decomposition for a proper modeling and analysis. This article addresses a comprehensive survey of the main time-series decomposition strategies and their relative performances in different application domains. The following strategies are discussed: i) Fourier Transform, ii) Wavelet transforms, iii) Moving Average, iv) Singular Spectrum Analysis, v) Lazy, vi) GHKSS, and vii) other approaches based on the Empirical Mode Decomposition method. In order to assess these strategies, we employ diverse and complementary performance measures: i) Mean Absolute Error, Mean Squared and Root Mean Squared Errors; ii) Minkowski Distances; iii) Complexity-Invariant Distance; iv) Pearson correlation; v) Mean Distance from the Diagonal Line; and vi) Mean Distance from Attractors. Each decomposition strategy is better devoted to particular scenarios, however, without any previous knowledge on data, GHKSS confirmed to work as a fair and general baseline besides its time complexity.     

Design guidelines for speech recognition interfaces

Seven classes of design guidelines are described for interfaces which use speech recognition. The guidelines concern: (i) allocation of function within complex systems; (ii) parallel processing of speech with other modalities; (iii) design of command vocabulary; (iv) choice of command syntax; (v) use of feedback; (vi) template and user training; and (vii) choice of evaluation criteria.     

Application of the observer/Kalman filter identification method to unknown time-delay disturbed systems and the associated optimal digital tracker design

ABSTRACT

Many practical systems are described as unknown multi-input-multi-output (MIMO) time-delay systems with unknown disturbances. The properties and performance of multi-time-delay MIMO systems are quite different compared to delay-free systems, in particular if the time-delay is long. In addition, for practical implementation digital controllers are often required instead of analog controllers. This paper presents: (i) an overview of the mathematical modelling based on the observer/Kalman filter identification (OKID) method, with some insight on known/unknown MIMO systems; (ii) a study of the pole-zero maps of the identified delay-free system and the known/unknown time-delay system for various sampling periods; (iii) a development of the explicit pole-zero map of the sampled-data MIMO neutral system with multiple discrete and disturbed time delays; (iv) an utilisation of the OKID method for known/unknown time-delay sampled-data MIMO systems with unknown disturbances; (v) an extension of the existing equivalent-input-disturbance (EID) estimator, to determine a robust tracker for the unknown time-delay system with unknown disturbances; and (vi) an investigation on the unknown time-delay system with stable or unstable zeros to determine which traditional or customised tracker can be applied. In addition, simulation studies are performed on the relationship between pole-zero maps and delay times.     

Robust estimation of a correlation coefficient for <Emphasis Type="Italic">ε</Emphasis>-contaminated bivariate normal distributions

Robust estimations of a correlation coefficient, based on: (i) direct robust analogues of a sample correlation coefficient, (ii) nonparametric estimations of correlation, (iii) robust regression, (iv) robust estimation of the major constituents of a variance, (v) stable estimation of parameters, and (vi) preliminary removal of outliers from data with the following application of a sample correlation coefficient to the residuary observations, are studied. Their application to contaminated normal models on small and large samples is studied, the best robust estimations from the suggested are pointed out.     

Exact Knowledge Hiding through Database Extension

In this paper, we propose a novel, exact border-based approach that provides an optimal solution for the hiding of sensitive frequent itemsets by (i) minimally extending the original database by a synthetically generated database part - the database extension, (ii) formulating the creation of the database extension as a constraint satisfaction problem, (iii) mapping the constraint satisfaction problem to an equivalent binary integer programming problem, (iv) exploiting underutilized synthetic transactions to proportionally increase the support of non-sensitive itemsets, (v) minimally relaxing the constraint satisfaction problem to provide an approximate solution close to the optimal one when an ideal solution does not exist, and (vi) by using a partitioning in the universe of the items to increase the efficiency of the proposed hiding algorithm. Extending the original database for sensitive itemset hiding is proved to provide optimal solutions to an extended set of hiding problems compared to previous approaches and to provide solutions of higher quality. Moreover, the application of binary integer programming enables the simultaneous hiding of the sensitive itemsets and thus allows for the identification of globally optimal solutions.     

DHLAS: A web-based information system for statistical genetic analysis of HLA population data

DHLAS (database HLA system) is a user-friendly, web-based information system for the analysis of human leukocyte antigens (HLA) data from population studies. DHLAS has been developed using JAVA and the R system, it runs on a Java Virtual Machine and its user-interface is web-based powered by the servlet engine TOMCAT. It utilizes STRUTS, a Model-View-Controller framework and uses several GNU packages to perform several of its tasks. The database engine it relies upon for fast access is MySQL, but others can be used a well. The system estimates metrics, performs statistical testing and produces graphs required for HLA population studies: (i) Hardy-Weinberg equilibrium (calculated using both asymptotic and exact tests), (ii) genetics distances (Euclidian or Nei), (iii) phylogenetic trees using the unweighted pair group method with averages and neigbor-joining method, (iv) linkage disequilibrium (pairwise and overall, including variance estimations), (v) haplotype frequencies (estimate using the expectation-maximization algorithm) and (vi) discriminant analysis. The main merit of DHLAS is the incorporation of a database, thus, the data can be stored and manipulated along with integrated genetic data analysis procedures. In addition, it has an open architecture allowing the inclusion of other functions and procedures.     

A framework for nonparametric profile monitoring

Control charts have been widely used for monitoring the functional relationship between a response variable and some explanatory variable(s) (called profile) in various industrial applications. In this article, we propose an easy-to-implement framework for monitoring nonparametric profiles in both Phase I and Phase II of a control chart scheme. The proposed framework includes the following steps: (i) data cleaning; (ii) fitting B-spline models; (iii) resampling for dependent data using block bootstrap method; (iv) constructing the confidence band based on bootstrap curve depths; and (v) monitoring profiles online based on curve matching. It should be noted that, the proposed method does not require any structural assumptions on the data and, it can appropriately accommodate the dependence structure of the within-profile observations. We illustrate and evaluate our proposed framework by using a real data set.     

Testing a finite-difference model for soil heat transfer by comparing numerical and analytical solutions

Soil temperature influences most physical, chemical and biological processes that occur in soil. Analytical models (AMs)—exact analytical solutions of the soil heat flux equation—only exist to very specific conditions. Finite-difference models (FDMs) are applicable to more general conditions. Since FDMs are not exact, but approximate, they must be preliminarily tested by comparing their simulated results with those from AMs, if available. In previous literature, such tests have been done briefly, mainly through data-plots. The general objective of this study was the development of comprehensive procedures to perform such tests: (i) comparison of models by data-plots and statistical indices; (ii) searching for sources of apparent errors (AEs)—a concept introduced here; (iii) determination of the number of simulated periods (years) that the model computer program should run before output is recorded; and (iv) studying the influence of time-step and space-step magnitudes on the model performance. An explicit one-dimensional FDM was tested. Simulations represented soils of different thermal properties. Simulation results showed that: (i) statistical indices values consistently quantified the qualitative data-plot observations; (ii) AEs could be corrected by considerations on the first soil-layer temperature and by choosing a sufficiently large simulation depth; (iii) no more than five periods are necessary for the FDM tested here; and (iv) adequate choice of time and space steps reduces errors and also reduces computing time.     

Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans

Surface soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition on a daily basis and on a regional scale (∼10 km). However, the effects of vegetation cover, soil temperature, snow cover, topography, and soil surface roughness also play a significant role in the microwave emission from the surface. Different soil moisture retrieval approaches have been developed to account for the various parameters contributing to the surface microwave emission. Four main types of algorithms can be roughly distinguished depending on the way vegetation and temperature effects are accounted for. These algorithms are based on (i) land cover classification maps, (ii) ancillary remote sensing indexes, and (iii) two-parameter or (iv) three-parameter retrievals (in this case, soil moisture, vegetation optical depth, and effective surface temperature are retrieved simultaneously from the microwave observations). Methods (iii) and (iv) are based on multiconfiguration observations, in terms of frequency, polarization, or view angle. They appear to be very promising as very few ancillary information are required in the retrieval process. This paper reviews these various methods for retrieving surface soil moisture from microwave radiometric systems. The discussion highlights key issues that will have to be addressed in the near future to secure operational use of the proposed retrieval approaches.     

Economic land evaluation for agricultural resource management in Northern Thailand

This paper describes a GIS-based system, EconSuit, that supports dynamic assessment of economic land suitability for major economic crops in Northern Thailand. Prior to economic land evaluation, a physical land suitability index is computed using a fuzzy set approach in GIS. The procedure bypasses crop modeling and permits suitability to be defined in a continuous scale with a graphic interface enabling a user to dynamically assign diagnostic factors and weights for physical land evaluation. Economic land evaluation is accomplished by assigning field survey data to land mapping units using spatial interpolation. Unit costs of inputs and outputs can be entered through the graphic user interface, allowing constant updates of economic values in the system. Scenarios of agricultural land use planning may be formulated by users depending on policy or economic circumstances.     

Cover. Wind energy mapping of coastal zones by synthetic aperture radar (SAR) for siting potential windmill locations

MERIS (Medium Resolution Imaging Spectrometer) is a fine spectral and medium spatial resolution satellite sensor and is part of the core instrument payload of Envisat, the European Space Agency's (ESA) environmental research satellite, launched in March 2002. Designed primarily for ocean (‘MER’) and coastal zone remote sensing, this imaging spectrometer (‘IS’) now has a much broader environmental remit covering also land and atmospheric applications. This paper reviews (i) MERIS's development history, focusing on its changing mission objectives; (ii) MERIS's technical specification, including its radiometric, spectral and geometric characteristics, programmability and onboard calibration; (iii) decisions that led to modifications of MERIS's spectral, geometric and radiometric performance for land applications; (iv) MERIS's data products; and (v) some of the ways in which MERIS data might be used to provide information on terrestrial vegetation.     

SPASS: a generic process-oriented crop model with versatile windows interfaces

Crop simulation models are now widely used in agronomy. A highly modular structure has been developed for crop growth simulation models based on the essential processes that are common to various plant species. This generic process-oriented modular structure was implemented in the SPASS (Soil-Plant-Atmosphere System Simulation) model using Microsoft Visual C/C++ under Windows. SPASS is composed of six parts: (i) data management, (ii) memory allocation/initialization, (iii) Modules for crop, soil and microclimate processes, (iv) model configuration and simulation, (v) graphic/table display and (vi) model knowledge integration. The model can be easily configured using the Windows interface by choosing various combinations of process modules developed based on different model approaches. Up to 30 graphic and 5 table MDI (Multiple Document Interface) child windows can be opened during or after the simulation run. Each child window can be resized freely on the screen for the convenience of data analysis. The model theory can be easily viewed using the online theory book. The SPASS model structure facilitates comparison of different model approaches, transfer of improvements between models, development of multi-crop models and modification of process modules for new applications.     

Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives

Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI) tools and data fusion strategies has recently opened new perspectives for environmental monitoring and assessment. This is mainly due to the advancement of machine learning (ML) and data mining approaches, which facilitate extracting meaningful information at a large scale from geo-referenced and heterogeneous sources. This paper presents the first review of AI-based methodologies and data fusion strategies used for environmental monitoring, to the best of the authors’ knowledge. The first part of the article discusses the main challenges of geographical image analysis. Thereafter, a well-designed taxonomy is introduced to overview the existing frameworks, which have been focused on: (i) detecting different environmental impacts, e.g. land cover land use (LULC) change, gully erosion susceptibility (GES), waterlogging susceptibility (WLS), and land salinity and infertility (LSI); (ii) analyzing AI models deployed for extracting the pertinent features from RS images in addition to data fusion techniques used for combining images and/or features from heterogeneous sources; (iii) describing existing publicly-shared and open-access datasets; (iv) highlighting most frequent evaluation metrics; and (v) describing the most significant applications of ML and data fusion for RS image analysis. This is followed by an overview of existing works and discussions highlighting some of the challenges, limitations and shortcomings. To provide the reader with insight into real-world applications, two case studies illustrate the use of AI for classifying LULC changes and monitoring the environmental impacts due to dams’ construction, where classification accuracies of 98.57% and 97.05% have been reached, respectively. Lastly, recommendations and future directions are drawn.     

Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter

Proper estimation of initial state variables and model parameters are vital importance for determining the accuracy of numerical model prediction. In this work, we develop a one-dimensional land data assimilation scheme based on ensemble Kalman filter and Common Land Model version 3.0 (CoLM). This scheme is used to improve the estimation of soil temperature profile. The leaf area index (LAI) is also updated dynamically by MODIS LAI production and the MODIS land surface temperature (LST) products are assimilated into CoLM. The scheme was tested and validated by observations from four automatic weather stations (BTS, DRS, MGS, and DGS) in Mongolian Reference Site of CEOP during the period of October 1, 2002 to September 30, 2003. Results indicate that data assimilation improves the estimation of soil temperature profile about 1 K. In comparison with simulation, the assimilation results of soil heat fluxes also have much improvement about 13 W m^− 2 at BTS and DGS and 2 W m^− 2 at DRS and MGS, respectively. In addition, assimilation of MODIS land products into land surface model is a practical and effective way to improve the estimation of land surface variables and fluxes.     

Learning Complex Tasks Using a Stepwise Approach

This paper explores a stepwise learning approach based on a system's decomposition into functional subsystems. Two case studies are examined: a visually guided robot that learns to track a maneuvering object, and a robot that learns to use the information from a force sensor in order to put a peg into a hole. These two applications show the features and advantages of the proposed approach: i) the subsystems naturally arise as functional components of the hardware and software; ii) these subsystems are building blocks of the robot behavior and can be combined in several ways for performing various tasks; iii) this decomposition makes it easier to check the performances and detect the cause of a malfunction; iv) only those subsystems for which a satisfactory solution is not available need to be learned; v) the strategy proposed for coordinating the optimization of all subsystems ensures an improvement at the task-level; vi) the overall system's behavior is significantly improved by the stepwise learning approach.     

基于“3S”技术的县级土地资源动态监测技术系统

土地资源日新月异的变化使得传统的土地资源管理手段无法满足土地资源信息及时更新的要求。探讨了利用遥感、地理信息系统和全球定位系统为代表的“3S”技术进行县级土地资源动态监测和更新的原理和方法。遥感是进行土地利用变化动态监测,发现土地利用变化区域的主要手段。全球定位系统接收仪用于对变化区域进行现场精确定位和实测。实测的变更数据可用于对原有的地理信息系统本底数据库进行修改,从而完成对土地资源的动态监测和数据库的及时更新。福清市土地资源动态监测技术系统就是利用“3S”技术建立起来的业务运行系统。系统所具备的功能可以满足县级土地资源的动态监测和及时更新。     

Ergonomics in sickle operation

Sickle operation in harvesting has been analysed with reference to design features of nine different types of sickles, and field and laboratory based investigations on biomechanical stresses and physiological valuation on six farmers. It has been indicated that the blade geometry contributes significantly to human performance and there is ample scope for further design optimisation. The suggested modifications are: (i) sickle weight - 200 g; (ii) total length of sickle - 33 cm; (iii) handle length - 11 cm; (iv) handle diameter - 3 c cm; (v) radius of blade curvature - 15 cm; (vi) blade concavity - 5 cm; (vii) serrated sickle: tooth pitch - 0.20 cm and tooth angle - 60 degrees; (viii) ratio of the length of cutting surface to chord length - 1.20.     

Six cognitive gaps by using TRIZ and tools for service system design

The purpose of this paper is to develop a conceptual framework of service system design. When reviewing the past literatures in the field of TRIZ solving processes, it was found that there were several drawbacks in the TRIZ solving processes which were: (i) the TRIZ depended on the researchers’ subjective viewpoints; (ii) the TRIZ lacked a customized contradictory matrix and innovative principles; (iii) and the innovative principle of TRIZ could not provide comprehensive solutions to apply to all service fields. In attempting to understand the reason for the additional above drawbacks, much of the literature was organized to find six cognitive gaps, which might support TRIZ implementation during the TRIZ solving processes for service system design (SSD). The six cognitive gaps which are: (i) identifying the opportunity by mapping the specific features and characteristics; (ii) the ideal contradiction matrix; (iii) evaluating the results especially by checking the KM-based profession-oriented tools; (iv) checking the KM-based context; (v) human factors and teamwork; (vi) innovative supplemental technologies. Finally, the six cognitive gaps were integrated into a conceptual framework of SSD development, which will provide an improved guide for further researchers.