Modelling water dynamics with DNDC and DAISY in a soil of the North China Plain: A comparative study

The performance of the DNDC and Daisy model to simulate the water dynamics in a floodplain soil of the North China Plain was tested and compared. While the DNDC model uses a simple cascade approach, the Daisy model applies the physically based Richard's equation for simulating water movement in soil. For model testing a three years record of the soil water content from the Dong Bei Wang experimental station near Beijing was used. There, the effect of nitrogen fertilization, irrigation and straw removal on soil water and nitrogen dynamics was investigated in a three factorial field experiment applying a split-split-plot design with 4 replications. The dataset of one treatment was used for model testing and calibration. Two other independent datasets from further treatments were employed for validating the models. For both models, the simulation results were not satisfying using default parameters. After parameter optimisation and the use of site-specific van Genuchten parameters, however, the Daisy model performed well. But, for the DNDC model, parameter optimisation failed to improve the simulation result. Owing to the fact that many biological processes such as plant growth, nitrification or denitrification depend strongly on the soil water content, our findings bring us to the conclusion that the site-specific suitability of the DNDC model for simulating the soil water dynamics should be tested before further simulation of other processes.     

Aligned texture map creation for pose invariant face recognition

In last years, Face recognition based on 3D techniques is an emergent technology which has demonstrated better results than conventional 2D approaches. Using texture (180° multi-view image) and depth maps is supposed to increase the robustness towards the two main challenges in Face Recognition: Pose and illumination. Nevertheless, 3D data should be acquired under highly controlled conditions and in most cases depends on the collaboration of the subject to be recognized. Thus, in applications such as surveillance or control access points, this kind of 3D data may not be available during the recognition process. This leads to a new paradigm using some mixed 2D-3D face recognition systems where 3D data is used in the training but either 2D or 3D information can be used in the recognition depending on the scenario. Following this concept, where only part of the information (partial concept) is used in the recognition, a novel method is presented in this work. This has been called Partial Principal Component Analysis (P²CA) since they fuse the Partial concept with the fundamentals of the well known PCA algorithm. This strategy has been proven to be very robust in pose variation scenarios showing that the 3D training process retains all the spatial information of the face while the 2D picture effectively recovers the face information from the available data. Furthermore, in this work, a novel approach for the automatic creation of 180° aligned cylindrical projected face images using nine different views is presented. These face images are created by using a cylindrical approximation for the real object surface. The alignment is done by applying first a global 2D affine transformation of the image, and afterward a local transformation of the desired face features using a triangle mesh. This local alignment allows a closer look to the feature properties and not the differences. Finally, these aligned face images are used for training a pose invariant face recognition approach (P²CA).     

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.     

Time Windows Based Dynamic Routing in Multi-AGV Systems

This paper presents a dynamic routing method for supervisory control of multiple automated guided vehicles (AGVs) that are traveling within a layout of a given warehouse. In dynamic routing a calculated path particularly depends on the number of currently active AGVs' missions and their priorities. In order to solve the shortest path problem dynamically, the proposed routing method uses time windows in a vector form. For each mission requested by the supervisor, predefined candidate paths are checked if they are feasible. The feasibility of a particular path is evaluated by insertion of appropriate time windows and by performing the windows overlapping tests. The use of time windows makes the algorithm apt for other scheduling and routing problems. Presented simulation results demonstrate efficiency of the proposed dynamic routing. The proposed method has been successfully implemented in the industrial environment in a form of a multiple AGV control system.     

Aeroelastic tailoring using lamination parameters

The aim of the present work is to passively reduce the induced drag of the rear wing of a Formula One car at high velocity through aeroelastic tailoring. The angle-of-attack of the rear wing is fixed and is determined by the required downforce needed to get around a turn. As a result, at higher velocity, the amount of downforce and related induced drag increases. The maximum speed on a straight part is thus reduced due to the increase in induced drag. A fibre reinforced composite torsion box with extension-shear coupled upper and lower skins is used leading to bending-torsion coupling. Three-dimensional static aeroelastic analysis is performed loosely coupling the Finite Element code Nastran and the Computational Fluid Dynamics panel code VSAERO using ModelCenter. A wing representative of Formula One rear wings is optimised for minimum induced drag using a response surface methodology. Results indicate that a substantial induced drag reduction is achievable while maintaining the desired downforce during low speed turns.     

Informationspflicht über den datenschutz im M-commerce

Das Bundesdatenschutzgesetz (BDSG) und das Telemediengesetz (TMG) verpflichten datenerhebende und -verarbeitende Stellen, bei Betroffenen Transparenz über die Verarbeitung ihrer Daten herzustellen. Nach § 13 Abs. 1 TMG hat der Anbieter über die Verarbeitung von Daten bei der Nutzung eines Teledienstes zu informieren. Nutzt der Betroffene ein internetf?higes Handy als mobiles Endger?t, ist das Darstellungsvolumen auf kleinen Displays allerdings beschr?nkt. Anbieter müssen sich bei der Umsetzung der bu?geldbewehrten Informationspflicht darauf einstellen.     

微机原理与应用课程改革与实践

为适应新技术对信息化人才培养要求,在分析微机原理与应用课程特点及教学状况的基础上,从教学目标、教学内容入手,阐述微机原理与应用课程改革思路和方法,从适应科技发展的角度,提出调整课程教学内容,构建微机原理与应用多媒体及网络教学平台系统,改革课程考试方式。     

An integrated case-based reasoning and MCDM system for Web based tourism destination planning

The accelerating interaction between technology and tourism has changed radically the efficiency and effectiveness of tourism organizations, as well as how consumers interact with organizations. In this study, a Web based intelligent framework for travel agencies is proposed that offers customers a fast and reliable response service in a less costly manner. The proposed framework integrates case-based reasoning (CBR) system with a well-known multi criteria decision making (MCDM) technique, namely Analytic Hierarchy Process, to enhance the accuracy and speed in case matching in tourism destination planning. The integration of two techniques enables taking advantages of their strengths and complements each other’s weaknesses. A case study is performed to demonstrate how this framework can facilitate intelligent decision support by retrieving best-fitted responses for customers.     

Derivation of empirical equations for neutronic performance in a thorium fusion breeder with various coolants using regression analysis

In this paper, regression analyses (RA) are presented for the neutronic calculation of ThO₂ mixed ²⁴⁴CmO₂ fuel with different neutronic parameters for various coolants, natural lithium, Li₂₀Sn₈₀ and Flinabe, respectively. The tritium breeding ratio (TBR), energy multiplication factor (M), total fission rate (Σ_f) and ²³²Th(n, γ) reaction is computed by XSDRNPM. In addition, this numerical results are estimated by RA depends on neutronic parameters and the empirical equations for neutronic performance are acquired. The results obtained by using XSDRNPM and the results of the RA, obtained empirical equations, are compared. The empirical equations indicate that RA can successfully be used for the prediction of the neutronic performance parameters in the hybrid reactor with a high degree of accuracy. In addition, correlation matrix is calculated to determined statistical relationships between variables TBR, M, Σ_f, and ²³²Th(n, γ).     

Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment

Sustainable supply chain management (SSCM) provides economic, social end environmental requirements in material and service flows occurring between suppliers, manufacturers and customers. SSCM structure is considered as a prerequisite for a sustainable success. Thus designing an effective SCM structure provides competitive advantages for the companies. In order to achieve an effective design of this structure, it is possible to apply quality function deployment (QFD) approach which is successfully applied as an effective product and system development tool. This study presents a decision framework where analytic network process (ANP) integrated QFD and zero-one goal programming (ZOGP) models are used in order to determine the design requirements which are more effective in achieving a sustainable supply chain (SSC). The first phase of the QFD is the house of quality (HOQ) which transforms customer requirements into product design requirements. In this study, after determining the sustainability requirements named customer requirements (CRs) and design requirements (DRs) of a SSC, ANP is employed to determine the importance levels in the HOQ considering the interrelationships among the DRs and CRs. Furthermore ZOGP approach is used to take into account different objectives of the problem. The proposed method is applied through a case study and obtained results are discussed.