BACKGROUND: Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. RESULTS: We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. CONCLUSIONS: We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

Predicting surrounding vehicle behavior plays an important role in an intelligent vehicle. Optimization of control strategy considering predicted future events could provide significant benefits by improving efficiency, comfort, and safety. However, realizing such prediction in an arbitrary environment is a challenging task as the real environment is highly diverse. In this paper, we propose a model-less location-based prediction method for a connected vehicle, which shares driving data through a cloud server. The shared data are stored in a relational database management system after associated with the location information. Surrounding vehicle behavior is then predicted with kernel density estimation by referring to nearby data, which implicitly reflect all location-dependent factors, such as road design, traffic rule, and region. Since this method does not rely on any pre-trained models, prediction performance is not affected by the overfitting issue. The performance of the proposed method has been evaluated by applying to optimization-based adaptive cruise control, which minimizes energy loss and a following error based on predicted future position of a preceding vehicle. The experimental result with urban driving data shows that the proposed method is more accurate and fuel efficient than several baseline models including kinematic model and neural networks.

In disaster areas, office buildings, or at home, multiple autonomous networked mobile robots may act instead of human beings. These robots have to move to their destiantion so as to perform their function. For this purpose they need to be able to recognize the changes in the surrounding environment. They are equipped with a motion-planning method in order to avoid in real time collisions with other robots or obtacles. In this paper we propose a motion planning method based on PRM (Probabilistic Roadmap) algorithm. To evaluate our method, we constructed an experiment platform based on StarBED, which is a large-scale network testbed. By using the virtual environment manager Map Manager, the WLAN emulator QOMET, and the experiment-support software RUNE we are able to perform emulation of large-scale autonomous networked mobile robot systems. The experimental results confirm the usefulness of collaborative motion planning, which results in reaching faster the estimation and in less frequent re-planning.

Inductive learning of first-order theory based on examples has serious bottleneck in the enormous hypothesis search space needed, making existing learning approaches perform poorly when compared to the propositional approach. Moreover, in order to choose the appropiate candidates, all Inductive Logic Programming (ILP) systems only use quantitive information, e.g. number of examples covered and length of rules, which is insufficient for search space having many similar candidates. This paper introduces a novel approach to improve ILP by incorporating the qualitative information into the search heuristics by focusing only on a kind of data where one instance consists of several parts, as well as relations among parts. This approach aims to find the hypothesis describing each class by using both individual and relational characteristics of parts of examples. This kind of data can be found in various domains, especially in representing chemical compound structure. Each compound is composed of atoms as parts, and bonds as relations between two atoms. We apply the proposed approach for discovering rules describing the activity of compounds from their structures from two real-world datasets: mutagenicity in nitroaromatic compounds and dopamine antagonist compounds. The results were compared to the existing method using ten-fold cross validation, and we found that the proposed method significantly produced more accurate results in prediction.

MOTIVATION: Chemical carcinogenicity is an important subject in health and environmental sciences, and a reliable method is expected to identify characteristic factors for carcinogenicity. The predictive toxicology challenge (PTC) 2000-2001 has provided the opportunity for various data mining methods to evaluate their performance. The cascade model, a data mining method developed by the author, has the capability to mine for local correlations in data sets with a large number of attributes. The current paper explores the effectiveness of the method on the problem of chemical carcinogenicity. RESULTS: Rodent carcinogenicity of 417 compounds examined by the National Toxicology Program (NTP) was used as the training set. The analysis by the cascade model, for example, could obtain a rule 'Highly flexible molecules are carcinogenic, if they have no hydrogen bond acceptors in halogenated alkanes and alkenes'. Resulting rules are applied to predict the activity of 185 compounds examined by the FDA. The ROC analysis performed by the PTC organizers has shown that the current method has excellent predictive power for the female rat data. AVAILABILITY: The binary program of DISCAS 2.1 and samples of input data sets on Windows PC are available at http://www.clab.kwansei.ac.jp/mining/discas/discas.html upon request from the author. SUPPLEMENTARY INFORMATION: Summary of prediction results and cross validations is accessible via http://www.clab.kwansei.ac.jp/~okada/BIJ/BIJsupple.htm. Used rules and the prediction results for each molecule are also provided.

This paper describes a new high-speed wireless LAN system in the 19-GHz band. This system features a maximum throughput of 15.2 Mbit/s, which is faster than Ethernet's. To achieve high speed wireless data transmission, we propose the new global scheduling multiple access (GSMA). This system consists of a control module (CM) and some user modules (UMS). One CM can communicate with up to ten UMs. This GSMA, in which all data transmission is managed in the CM, has both short-length and long-length frames, and concatenates multiple packets, so that it performs well with light traffic and heavy traffic: with minimal delay. For high-speed transmission and easy construction, DQPSK/differential detection is used as a modulation/demodulation scheme, and both FEC and ARQ are adopted as the error control method. In addition, as a countermeasure to shadowing and multipath fading, the CM has an omnibeam antenna, and each UM uses a 12-sector antenna, and sector selection diversity is carried out at the UM. This wireless LAN system is based on the RCR STD-34A Japanese radio system standard.

In this paper, we propose the concept of robotic task design for task directed robotics in order to develop a framework for achieving versatile robotic task execution system. This system consists of the following subsystems: 1) a robotic task design system; 2) a teaching (programming) system; and 3) a task execution system. These have equal importance in accomplishing the task intended by the user with the robot system instead of over emphasizing the task execution system which has been the main research area of robotics in terms of motion control. Based on this proposed concept, a robotic task execution controller called NOAC is designed. The main concept in the structure of NOAC is to separate function (1) which is commonly needed by every task in the task class (task class function) and function (2) which is a task-oriented function. They are to be parallel processed on NOAC system. This separation simplifies software development. A deburring task is designed based on the nature of the burr and implemented onto the NOAC system with the above concept. This system is shown to be simple and expandable.>

This paper describes a robust and broadly applicable path-finding method that uses the range data of object surfaces adjacent to the path. The proposed method locates the path by using the shape defined by multiple sequences of less noisy parts of the range data. It is based on an originally devised algorithm for matching the shape of the object to that of the prototype model. This shape-matching algorithm can define the degree of similarity for curved surfaces three-dimensionally without assuming that the surface consists of parametric surfaces, such as planes, or conic surfaces. Segmentation of the surface is therefore not required. Moreover, this similarity can be defined even if the processing extent is not determined in advance. Consequently, the position of the paths used for robotic movement can be determined accurately from matching information by using less noisy parts of the range data, without putting restrictions on the shape of the object.>

Although much research has been carried out on feature extraction from 3D object, validity and appropriateness of matching measures have not been given enough consideration. In 3D object recognition, object features are often represented by directional data, and treatment of such data needs different operation from that of the data on Euclidean space. In this paper, the authors propose a new method to define a matching measure between objects, and show that this represents the similarity reasonably well with good robustness. Moreover they show that the reliability of the partially input data can be estimated as a by-product of this method.>