Towards integrating GIS and catchment models

Modeling the impact of non-point source pollution in catchments is a complex problem, and one that has troubled natural resource managers for many years. The development of spatially distributed hydrologic models has led to improved model forecasting at the cost of requiring more detailed spatial information. In addition, the analysis is much more sensitive to errors in the data. Incorporation of catchment models into a Geographical Information System (GIS) has improved matters by streamlining data input and providing better interpretation of model outputs. This paper reviews different strategies for linking a catchment model with GIS. It examines data issues related to the performance of models and how well they match physical landscape conditions. Integration with GIS is shown to be necessary for the efficient and proper operation of models in resource management situations. The paper concludes that tighter integration between generic sub-models for physical landscape processes and GIS is still required.     

Production control: Current developments and future trends

Manufacturing industry is going through a period of unprecedented change as a result of the developments in micro-electronic technology. This is bringing about a transformation not only in hardware, in the form of computer-aided manufacturing, but in the manner in which production is organised and controlled.It is in the latter connection that the present paper sets out to review current developments and future trends. In particular the machine readable bar-code is seen as a key element in extending computerised control to cover not only the machines, but tools, materials and parts and their movement in the development of fully integrated manufacturing systems.As the move towards automation gathers pace it is forecast that manufacturing industry will become more process industry like in form, with consequences for the way in which production is organised and controlled.Finally consideration is given to the manner in which these current developments in the field of production are giving rise to a structural alteration in industrial labour requirements in which, in the future, fewer people will be required but on highly enriched work tasks.     

Current status and future trends in machine translation

A survey of the current machine translation systems is given, which includes not only activities in Japan, but also abroad, especially European, US and Canadian activities. Then the components of a machine translation system are explained from the standpoint of software, linguistic components, and users' demands. The importance of pre-editing and post-editing is stressed. The semantic and contextual processings are essential to obtain a better translation quality, which are the future problems to attack. Attention is given to the difficulty of contemplating a pivot method in machine translation instead of transfer methods, because the projection from a word or a phrase to a concept is very difficult if we want to have a very exact concept representation and translation. A new transfer method which accompanies the pe-transfer structural adjustment and post-transfer adjustment is explained. This method was adopted by the Japanese governmental project of machine translation which was directed by the author. Various mechanisms of structural transformations in the transfer and generation processes are explained, which are necessitated by the language translation between the two languages of different language families like Japanese and English.Finally some comments are given from the standpoint of users of machine translation systems. Systems always are imperfect, and users must use them after recognizing the possibilities and the limitations of the system.     

Advancing scenario planning through integrating urban growth prediction with future flood risk models

High uncertainty about future urbanization and flood risk conditions limits the ability to increase resiliency in traditional scenario-based urban planning. While scenario planning integrating urban growth prediction modeling is becoming more common, these models have not been effectively linked with future flood plain changes due to sea level rise. This study advances scenario planning by integrating urban growth prediction models with flood risk scenarios. The Land Transformation Model, a land change prediction model using a GIS based artificial neural network, is used to predict future urban growth scenarios for Tampa, Florida, USA, and future flood risks are then delineated based on the current 100-year floodplain using NOAA level rise scenarios. A multi-level evaluation using three urban prediction scenarios (business as usual, growth as planned, and resilient growth) and three sea level rise scenarios (low, high, and extreme) is conducted to determine how prepared Tampa's current land use plan is in handling increasing resilient development in lieu of sea level rise. Results show that the current land use plan (growth as planned) decreases flood risk at the city scale but not always at the neighborhood scale, when compared to no growth regulations (business as usual). However, flood risk when growing according to the current plan is significantly higher when compared to all future growth residing outside of the 100-year floodplain (resilient growth). Understanding the potential effects of sea level rise depends on understanding the probabilities of future development options and extreme climate conditions.     

A minimally invasive model data passing interface for integrating legacy environmental system models

This paper presents the Model Data Passing Interface (MODPI). The approach provides fine-grained, multidirectional feedbacks between legacy environmental system models through read and write access to relevant model data during simulation using a bidirectional, event-based, publish-subscribe system with a message broker. MODPI only requires commented directives in the original code and an XML linkage file with an optional custom data conversion module. Automated code generation, compilation, and execution reduce the programming burden on the modeler. Case study results indicated that MODPI required less code modifications within each model code base both before and after automated code generation, outperforming a baseline subroutine approach. Performance overhead for MODPI was minimal for the use case, offering speedup in some cases through parallel execution. MODPI is much less invasive than other techniques, potentially encouraging adoption by the modeling community in addition to maintainability and reusability of integrated model code.     

Optimal estimation control strategies for dynamic economic models with applications to environmental modelling

As controlled experimentation becomes more common in economics, it behoves economists to pay more attention to the peculiarities of economic experimentation. In particular, while most classical experimentation is essentially atemporal, economic experiments occur over a period of time, because individual economic units must adjust their activities to react to the incentive structure of the experiments. In this paper the choice of control strategies to improve estimation of the parameters in dynamic economic models with time-varying parameters is considered. Since restrictions of coefficients implied by the model structure cause the parameters also to appear in the solution of the estimation control problem, a sequential procedure immediately suggests itself. The combination of sequential estimation and design control strategies is shown to feature a marked improvement in the behavior of estimates over the nonsequential formulation. The maximum accuracy control problem considered in this paper can also be treated as an initial phase of a stochastic control problem. This will avoid solution to a difficult dual control problem. Finally, examples are presented illustrating the improvement in estimation accuracy that can be obtained.     

New trends on e-Procurement applying semantic technologies: Current status and future challenges

The present paper introduces and reviews existing technology and research works in the field of e-Procurement. More specifically this survey aims to collect those relevant approaches that have tackled the challenge of delivering more advanced and intelligent e-Procurement management systems due to its relevance in the industry to afford more timely, adaptable and flexible decisions in purchasing processes. Although existing tools and techniques have demonstrated their ability to manage e-Procurement processes as a part of a supply management system there is a lack of interoperability among tools, tangled dependencies between processes or difficulties to exploit existing data and information to name a few that are preventing a proper use of the new dynamic and data-based environment. On the other hand semantic-based technologies emerge to provide the adequate building blocks to represent domain-knowledge and elevate the meaning of information resources through a common and shared data model (RDF) with a formal query language (SPARQL) and accessible via the Internet Protocols. In this sense the Linked Data effort has gained momentum to apply the principles of the aforementioned initiative to boost the re-use of information and data across different tools and processes. That is why authors review both existing open issues in the context e-Procurement with special focus on public procurement and semantic-based approaches to address them. To do so a preliminary research study is conducted to assess the state of the art in the context of e-Procurement and semantic-based systems. Afterwards main drawbacks of existing e-Procurement systems are presented to narrow down in semantic-based approaches applied to this field. Once the current status in both areas is reviewed, authors purpose the use and creation of an e-Procurement index to evaluate the quality of service of procurement systems. In this light the Analytical Hierarchy Process (AHP) method is used to set up an initial weight for each indicator in the index and to perform a first comparison between traditional and semantic-based approaches. Finally some discussion, conclusions and future challenges are also outlined.     

Categorical and dimensional affect analysis in continuous input: Current trends and future directions

In the context of affective human behavior analysis, we use the term continuous input to refer to naturalistic settings where explicit or implicit input from the subject is continuously available, where in a human–human or human–computer interaction setting, the subject plays the role of a producer of the communicative behavior or the role of a recipient of the communicative behavior. As a result, the analysis and the response provided by the automatic system are also envisioned to be continuous over the course of time, within the boundaries of digital machine output. The term continuous affect analysis is used as analysis that is continuous in time as well as analysis that uses affect phenomenon represented in dimensional space. The former refers to acquiring and processing long unsegmented recordings for detection of an affective state or event (e.g., nod, laughter, pain), and the latter refers to prediction of an affect dimension (e.g., valence, arousal, power). In line with the Special Issue on Affect Analysis in Continuous Input, this survey paper aims to put the continuity aspect of affect under the spotlight by investigating the current trends and provide guidance towards possible future directions.     

Integrative connectionist learning systems inspired by nature: current models, future trends and challenges

The so far developed and widely utilized connectionist systems (artificial neural networks) are mainly based on a single brain-like connectionist principle of information processing, where learning and information exchange occur in the connections. This paper extends this paradigm of connectionist systems to a new trend—integrative connectionist learning systems (ICOS) that integrate in their structure and learning algorithms principles from different hierarchical levels of information processing in the brain, including neuronal-, genetic-, quantum. Spiking neural networks (SNN) are used as a basic connectionist learning model which is further extended with other information learning principles to create different ICOS. For example, evolving SNN for multitask learning are presented and illustrated on a case study of person authentification based on multimodal auditory and visual information. Integrative gene-SNN are presented, where gene interactions are included in the functioning of a spiking neuron. They are applied on a case study of computational neurogenetic modeling. Integrative quantum-SNN are introduced with a quantum Hebbian learning, where input features as well as information spikes are represented by quantum bits that result in exponentially faster feature selection and model learning. ICOS can be used to solve more efficiently challenging biological and engineering problems when fast adaptive learning systems are needed to incrementally learn in a large dimensional space. They can also help to better understand complex information processes in the brain especially how information processes at different information levels interact. Open questions, challenges and directions for further research are presented.     

Computational models for integrating linguistic and visual information: A survey

This paper surveys research in developing computational models for integrating linguistic and visual information. It begins with a discussion of systems which have been actually implemented and continues with computationally motivated theories of human cognition. Since existing research spans several disciplines (e.g., natural language understanding, computer vision, knowledge representation), as well as several application areas, an important contribution of this paper is to categorize existing research based on inputs and objectives. Finally, some key issues related to integrating information from two such diverse sources are outlined and related to existing research. Throughout, the key issue addressed is the correspondence problem, namely how to associate visual events with words and vice versa.     

MCE-RISK: integrating multicriteria evaluation and GIS for risk decision-making in natural hazards

During the past two decades there have been a wide range of applications for decision-making linking multicriteria evaluation (MCE) and geographic information systems (GIS). However, limited literature reports the development of MCE-GIS software, and the comparison of various MCE-GIS approaches. This paper introduces an MCE-GIS program called MCE-RISK for risk-based decision-making. It consists of a series of modules for data standardisation, weighting, MCE-GIS methods, and sensitivity analysis. The program incorporates different MCE-GIS methods, including weighted linear combination (WLC), the technique for order preference by similarity to ideal solution (TOPSIS), and compromise programming (CP), enabling comparisons between different methods for the same decision problem to be made. An example of decision-making for determining priority areas for a bushfire hazard reduction burning is examined. After implementing the alternative MCE-GIS methods, and comparing final outputs and the computational difficulty involved in the analysis, WLC is recommended. Some caveats on using MCE-GIS methods are also discussed. Although the development of MCE-RISK and its application reported in this paper are specific to risk-based decision-making in natural hazards, the program can be used for other environmental decision applications, such as environmental impact assessment and land-use planning.     

Current status and future trends of CIM in the Japanese steel industry

The growth of the Japanese steel industry has been supported by three factors: operation, equipment, and management technology. Computer-integrated manufacturing (CIM) is the core of management technology. CIM's purpose is to improve production efficiency through computer integration of data covering the staff (design and production control) and management department as well as the production line process department (order entry, manufacturing, and shipment). In this paper, an overview of the Japanese steel industry's system development process is given and the steps taken in order to establish the CIM system are described. The organization, operation, and improvement of operational capability, along with marketing and production-related systems, are integrated. Furthermore, an integrated system model is given as a CIM conceptual model. In closing, reference is also made to the challenges facing CIM in the Japanese steel industry as well as future directions to be taken.     

Ensembles for feature selection: A review and future trends

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good results. Normally, it has been commonly employed for classification, but it can be used to improve other disciplines such as feature selection. Feature selection consists of selecting the relevant features for a problem and discard those irrelevant or redundant, with the main goal of improving classification accuracy. In this work, we provide the reader with the basic concepts necessary to build an ensemble for feature selection, as well as reviewing the up-to-date advances and commenting on the future trends that are still to be faced.     

The study on integrating WebQuest with mobile learning for environmental education

This study is to demonstrate the impact of different teaching strategies on the learning performance of environmental education using quantitative methods. Students learned about resource recycling and classification through an instructional website based on the teaching tool of WebQuest. There were 103 sixth-grade students participating in this study and broken down into three groups: traditional instruction, traditional instruction with WebQuest and WebQuest instruction with outdoors. The major contribution of this study is the introduction of WebQuest into the outdoor instruction. The results of this study show that using WebQuest in outdoor instruction influences students’ learning performance positively. Two other interesting results are: (1) when WebQuest was used in real situations, students could acquire more knowledge and experiences, and (2) in the learning activity of the experiment, the students accomplished different learning tasks and expressed their own opinions and perspectives, which could foster their critical thinking skills. On the other hands, the students in outdoor situation could be positive to participate in learning activity; furthermore, they could ponder the learning contents by observing the real context and then they began to classify/categorize the resources. These findings will contribute to the development of teaching and learning for government, schools and teachers; for instance, teachers act as assistants or tutors and provide students with others public network resources, including PDAs, smartphones, tablet personal computers or the Internet, to improve their learning in outdoor learning environments, such as campus, museums or zoos.     

Current status and future trends in the automation of mineral and metal processing

A review of the current status and future trends in the automation and control of mineral and metal processing is presented. An evaluation of publications on IFAC MMM events during the last 20 years shows some trends in the application of a number of process control methods in the MM industry. Classical control has seen an extraordinarily wide application, but its inability to solve all the application problems of interest has led to further developments in control system methodologies and theory. One aim of this paper is to review the success of the translation of theoretically based developments into practice. Finally, the importance of information technology for the MM industry and process automation is highlighted and discussed.     

VVF: integrating modelling and GIS in a software tool for habitat suitability assessment

A computer program, VVF, has been developed to assess the suitability of a territory as habitat for a species. It integrates several types of Habitat Suitability models into a Geographical Information System. In addition to standard GIS functions, VVF allows a user to create, modify and store new Habitat Suitability models for different species, to create Habitat Suitability maps by running Habitat Suitability models for specific areas, and to process these maps. The program requires only basic GIS knowledge and is very flexible, so as to guarantee a broad applicability. VVF is aimed at assisting public decision-makers and conservation biologists to assess the viability of endangered and threatened species, to evaluate policies and plans for wildlife management, as well as species translocations, reserve design and habitat protection. VVF can also be used in Environmental Impact Assessment. In this paper, VVF is demonstrated with an application for Ibex.     

Requirement management for product-service systems: Status review and future trends

Many manufacturers today are striving to offer high value-added product-service systems (PSS) to their customers. Since the success of the PSS largely depends on the understanding and satisfying of different requirements, requirements management (RM) has become a critical factor in PSS development. However, PSS are mixed product-service offerings with features of heterogeneity, interaction, stakeholder participation and customization, which makes the PSS requirement difficult to be captured, analyzed, concretized and forecasted. Thus, this research analyzes the state of the art of requirements management for PSS by reviewing extensive literature of requirement identification, analysis, specification, and forecast. Based on the review, key challenges and future research directions are identified, and they are requirement elicitation considering interactions, hybrid PSS conflict detection and resolution, dynamic requirement forecasting with big data, smart requirement management and proactive response. This literature review lays the foundation for future research of PSS development.     

Ubiquitous robotics: Recent challenges and future trends

Ambient intelligence, ubiquitous and networked robots, and cloud robotics are new research hot topics that have started to gain popularity among the robotics community. They enable robots to acquire richer functionalities and open the way for the composition of a variety of robotic services with three functions: semantic perception, reasoning and actuation. Ubiquitous robots (ubirobots) overcome the limitations of stand-alone robots by integrating them with web services and ambient intelligence technologies. The overlap that exists now between ubirobots and ambient intelligence makes their integration worthwhile. It targets to create a hybrid physical–digital space rich with a myriad of proactive intelligent services that enhance the quality and the way of our living and working. Furthermore, the emergence of cloud computing initiates the massive use of a new generation of ubirobots that enrich their cognitive capabilities and share their knowledge by connecting themselves to cloud infrastructures. The future of ubirobots will certainly be open to an unlimited space of applications such as physical and virtual companions assisting people in their daily living, ubirobots that are able to co-work alongside people and cooperate with them in the same environment, and physical and virtual autonomic guards that are able to protect people, monitor their security and safety, and rescue them in indoor and outdoor spaces. This paper introduces the recent challenges and future trends on these topics.