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Background Urinary liver-type fatty acid-binding protein (L-FABP) is a well-known marker of proximal tubular impairment. We evaluated the relationship between cardiovascular disease (CVD) risk factors and levels of L-FABP in a cross-sectional community-based study. Participants with normoalbuminuria and normal estimated glomerular filtration rate (eGFR), that is, non-chronic kidney disease (non-CKD), were enrolled in this study. To the best of our knowledge, this is the first study to focus on the association between CVD risk factors and a proximal tubular marker in the Japanese general population with normoalbuminuria and normal eGFR. Methods The present study is part of the Sasayama study. The participants included 1000 community residents (447 men and 553 women) aged 40–64 years without a history of CVD or renal dysfunction. Out of these participants 375 men and 477 women, defined as non-CKD, were included for further analysis. In each sex, the highest quintile group was considered to have high-normal L-FABP levels. A multiple logistic regression model was used to evaluate the relationship between risk factors for CVD and high-normal L-FABP levels in the non-CKD participants. We performed a similar analysis using the high-normal urinary albumin to creatinine ratio (UACR) as a dependent variable instead of L-FABP. Results Among the non-CKD participants, in the highest quintile group (Q5, top 20%), L-FABP was ≥2.17 μg/gCre in men and ≥ 2.83 μg/gCre in women. In women, the multivariate odds ratio was 3.62 (1.45–9.00) for high-normal L-FABP in the presence of diabetes mellitus (DM) compared with that in the group without DM. However, the relationship between DM and the UACR level was not significant. In men, DM was significantly associated with high-normal UACR. However, the relationship with L-FABP levels was not significant. Conclusions The presence of DM was more strongly related to high-normal L-FABP levels than to high-normal UACR in women even at the stage of normoalbuminuria and normal eGFR. Our results were also consistent with the findings of a previous study where women were more prone to nonalbuminuric renal impairment compared to men, although further studies are required to confirm the results.

BACKGROUND Plasma concentration of n-3 polyunsaturated fatty acids (PUFAs) has been reported to be associated with renal function in Western populations. However, few studies have investigated the association between serum long-chain n-3 and n-6 PUFA profiles and renal function in a Japanese population with high marine-derived long-chain n-3 PUFA intake. METHODS A cross-sectional study was performed in 549 Japanese rural community-dwellers aged 40 to 64 years. In adjusted analysis of covariance, we assessed the relationship between estimated glomerular filtration rate (eGFR) and tertiles of serum long-chain n-3 and n-6 PUFA profiles ([eicosapentaenoic acid {EPA} + docosahexaenoic acid {DHA}]:arachidonic acid [AA]). GFR was estimated by Japanese specific equations using serum creatinine and cystatin C (eGFRcre and eGFRcys). Using multivariate-adjusted linear regression models, we also assessed the relationships between eGFRs and several n-3 and n-6 PUFAs, which have been suggested to be associated with renal function. RESULTS In all participants, higher dietary fish intake as assessed by a semi-quantitative questionnaire was associated with higher serum value of (EPA+DHA):AA. Participants in the higher (EPA+DHA):AA tertiles had non-significantly higher eGFRcre and significantly higher eGFRcys (P = 0.016). In addition, eGFRcys in T2+T3 of (EPA+DHA):AA was significantly higher than that in T1 (adjusted mean eGFRcys, T1: 87 ml/min/1.73 m(2), T2+T3: 91 ml/min/1.73 m(2); P < 0.01). Among the PUFAs, only (EPA+DHA) was significantly associated with eGFRcys. CONCLUSIONS Serum (EPA+DHA):AA, which reflects an individual's fish intake, might be associated with eGFRcys in Japanese community-dwellers.

Although chemotherapy is an important method for the treatment of patients with cancer, its efficacy is limited because of different sensitivities of tumor cells to anticancer agents and/or side effects on normal tissues. The present work demonstrates that mitochondria play a crucial role in the apoptosis of cancer cells induced by anticancer agents that interact with DNA but not with the cytoskeleton. Agents that interact with DNA selectively enhanced generation of reactive oxygen species (ROS) in mitochondria, released cytochrome c, and activated caspase-9 and caspase-3 to induce apoptosis of mesothelioma H2052 cells but not their ρ(0) cells, which lack mitochondrial DNA (mtDNA). The sensitivity of a variety of cells to the agents showed positive correlation with the amounts of their mitochondria. In contrast, agents that selectively affect the cytoskeleton activated caspase-8 and caspase-3 and equally induced apoptosis of both H2052 and their ρ(0) cells by a mitochondria-independent mechanism. The results suggest that mtDNA is a potential target for the anticancer agents that interact with DNA to induce ROS-dependent apoptosis of cancer cells, whereas agents that affect the cytoskeleton induce cell death by a mitochondria- and ROS-independent mechanism. The present observation is important for the selection of medicine for chemotherapy of patients with cancer.

In the last two decades, it becomes possible to automate operations of various steel plants especially in rolling mills. As the results, stabilization of productivity and improvement of product quality have been attained. On the while, in these years, many skilled engineers and operators who actively promoted economical growth of steel industries will retire due to their age limits. Thus, the inheritance of the high level technology and know-how has becomes a serious problem. To overcome the problem, it is necessary to extract knowledge of the skilled persons and make technical textbook reducing tacit knowledge. In this paper, rules are extracted from the operation data of hot strip rolling applicable to the operation diagnosis and intervention during operation. To attain the object, agent based simulator of hot strip rolling has been developed to prepare various rolling data for extraction of diagnosis and intervention rules in rolling operations. As for the selection of normal and abnormal data, SVM algorithm is tested before rules extraction. Rules are written in Fuzzy logic forms and its parameters are optimized by GA algorithm. These technologies are involved in the operation support agent system of hot strip rolling mills together with RNN for automatic gain tuning of mill controller.

Plant control systems are now highly automated and are used in many industries. The control performance changes with the passage of time because of deterioration of plant facilities. For this reason, human experts tune the control system to improve overall plant performance. In this study, a PID control system for the oil refining chemical plant process is discussed. In oil refining, thousands of control loops are used in plants in order to keep the product quality at the desired value and to assure the safety of plant operation. Due to the ambiguity of the interference between control loops, it is difficult to estimate the plant dynamical model accurately. Using a neuro emulator and a recurrent neural networks model (RNN model) for emulation and tuning of parameters, a PID gain tuning system for a chemical plant controller is constructed. Numerical experiments using actual plant data demonstrate the effect of the proposed method. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(4): 28–36, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20973

This paper presents an optimisation model and its effective solution technique using beam search heuristic for floor-storage warehousing systems. For a floor-storage system, storage can be accessed from the top of stacks only. The objective is to minimise the number of re-handling operations by optimally determining the storage location and by grouping products for each customer that fit a given sequence for receiving and retrieving operations. An integer programming model is formulated and an approximate solution technique based on the beam search method is proposed to solve the problem by incorporating effective heuristics to reduce the search space using future receiving and retrieving requests. The effectiveness of the proposed method is demonstrated for industrial warehousing problems in a steel plant with 58 storage areas involving more than 3000 retrieving operations. The proposed solution method is shown to be more efficient than the traditional branch-and-bound method for solving integer programm...

Plant control systems are now highly automated and are used in many industries. The control performance changes with the passage of time because of deterioration of plant facilities. For this reason, human experts tune the control system to improve overall plant performance. In this study, a PID control system for the oil refining chemical plant process is discussed. In oil refining, thousands of control loops are used in plants in order to keep the product quality at the desired value and to assure the safety of plant operation. Due to the ambiguity of the interference between control loops, it is difficult to estimate the plant dynamical model accurately. Using a neuro emulator and a recurrent neural networks model (RNN model) for emulation and tuning of parameters, a PID gain tuning system for a chemical plant controller is constructed. Numerical experiments using actual plant data demonstrate the effect of the proposed method. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(4): 28–36, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20973

In real transportation environments for semi-conductor manufacturing, unexpected disturbances such as motion delays and/or sudden requests for transportation may cause collisions among AGVs increasing total transportation time. It is required to generate a collision-free routing for multiple automated guided vehicles (AGVs) within a few seconds. In this paper, we propose a distributed routing method under motion delay disturbance for multiple AGVs. The proposed method features a characteristic that each AGV subsystem derives its optimal route to minimize the sum of the transportation time and the penalties with respect to collision probability with other AGVs. The penalties reflect the collision probability distribution function for motion delay disturbances. The proposed method is applied to a routing problem for transportation in the semiconductor fabrication bay with 143 nodes and 20AGVs. The computational results show that the total transportation time obtained by the proposed method is shorter than that of the conventional method. For dynamic transportation environments, an optimal timing for re-routing multiple AGVs under motion delay disturbance is systematically determined by the tradeoff relationship between the total computation time to derive a solution and the uncertainties for re-routings. Markov chain is used to represent uncertainty distribution for re-routings. The proposed method is implemented in an experimental transportation system with 51 nodes and 5AGVs. The experimental results demonstrate that the proposed method is applicable to real transportation environments.

In this paper, we propose an inventory control method for supply chain planning for single stage production systems under uncertain demand. The planning and scheduling model for uncertain demand situation is developed to represent actual environments for planning and scheduling where variances of future demand are gradually available as the time proceeds. In the proposed method, the amount of shipping for raw materials and safety stock for final products are determined by a stochastic approach by using demand average and variation in feedforward. The quantity of safety stock of inventory for final products is determined by incorporating feedback information from the detailed scheduling results. The effectiveness of the proposed methods is investigated from numerical simulations.

In steel works, iron ores are stored in ore yard and sent to various plants in down stream according to transfer requests. To attain stable operations of steel works, it is necessary to keep a certain safety stock level in ore tanks of all plants. To attain this purpose, a novel ore transfer routing method has been developed based on a decentralized agent method. In case of disaster such as a big earthquake or a big fire, damages in the facilities of industrial complex may be unavoidable. In this paper, a decentralized optimization method is tested if it is able to cope with such emergencies. A decentralized agent corresponding to the kind of ore makes its own transfer route plan exchanging information with others. As the application of the proposed method, transfer scheduling in ore yard in steel works are made in case of destruction of the transfer facilities. Further, application of the proposed route planning method to the analysis of distribution of transfer loads for conveyers is described.

The enantioselective total synthesis of preclavulone-A methyl ester and its diastereomer was achieved from enantiomerically pure 5 in a stereocontrolled manner. The absolute stereochemistry of naturally occurring preclavulone-A methyl esters was determined by comparison of the [α]D value.

This paper addresses a local rescheduling procedure for a distributed routing system of multiple Automated Guided Vehicles in dynamic environments where the requests for transportation are given in real time. The effectiveness of the proposed method is investigated for a transportation system of 143 nodes and 10–30 AGV systems in a semi-conductor fabricating bay. A distributed and parallel routing system (DPRS) is implemented and tested on an experimental five mobile robot system. The results demonstrate that the proposed method can reduce the total computation time by 39% compared with the conventional method without lowering the performance level. It is also experimentally verified that the rescheduling procedure can deal with disturbances in a significantly short computation time.

In this paper, we propose a decomposition and coordination method for routing problems for multiple automated guided vehicles (AGVs) using Petri Nets. An extended Petri Net model is created to represent concurrent motion of multiple AGVs. The routing problem to minimize total transportatin time is formulated by the proposed Petri Net. The optimization model for the Petri Net is decomposed into several subproblems which can be solved by Dijkstra's algorithm in polynomial order. The effectiveness of the proposed method is evaluated by several numerical examples.

This paper describes the integrated quality control in consideration of equipment diagnosis and control of looper height in hot strip rolling. In the integrated platform, deterioration of the induction motor is recognized quantitatively and tendency management of the deterioration is carried out. Simultaneously, the looper height control in consideration of the material quality change and degree of deterioration is performed. The motor deterioration by breakage of the rotor bar is adaptively estimated from change of the rotor bar resistance. The change of material quality and the deterioration condition are regarded as change of the damping coefficient of the looper dynamic model. The adaptive type robust control is applied to the looper height control. The validity is verified by numerical simulation.

Recently, various studies have been undertaken to examine the inner furnace phenomena of a blast furnace. These studies are limited, however, to the evaluation of furnace performance while a designed method of furnace control remains unfulfilled. This paper proposes a method of estimating temperature distribution in a reactor furnace using boundary data in order to control inner target furnace temperature distribution. Initially, a simplified furnace simulation program for the calculation of inner furnace gas flow, pressure and temperature distribution is developed. Following that, the simulator is used to estimate and control inner furnace temperature distribution. In the estimation, boundary data such as temperature and pressure, measured near a furnace wall, are used in the furnace simulation. Then, for the control of inner furnace temperature distribution, necessary values for gas blowing at the bottom of the furnace and the burden supply at the top of the furnace are used. Both for the estimation and the control of the inner furnace temperature distribution, a sequential quadratic programming method, a method of the iterative optimization, is applied. In our method, an estimation of inner furnace temperature distribution is done as preparation for the determination of furnace control variables. Through numerical experiments, the validity of our method is demonstrated, showing that the temperature distribution in a furnace can be regulated to the desired one after iterative control operations.

In this paper, we propose an autonomous decentralized optimization system for multi-stage production processes. The proposed system consists of a Material Requirement Planning subsystem, a Distribution Planning subsystem and Decentralized Scheduling subsystems belonging to each production stage that constitute the entire plant. In the proposed system, each subsystem repeats both optimization of the schedule at each subsystem and data exchange among the subsystems. Computational results demonstrate that the results of the proposed planning system are superior to those of the hierarchical planning system, despite the fact that the proposed system has wide flexibility for adding the constraints and modifying the criterion of performance evaluation.

The paper describes an augmented Lagrangian decomposition and coordination approach for solving single machine scheduling problems to minimize the total weighted tardiness. The problem belongs to the class of NP-hard combinatorial optimization problem. We propose an augmented Lagrangian decomposition and coordination approach, which is commonly used for continuous optimization problems, for solving scheduling problems despite the fact that the problem is nonconvex and non-differentiable. The proposed method shows a good convergence to a feasible solution without heuristically constructing a feasible solution. The performance of the proposed method is compared with that of an ordinary Lagrangian relaxation.

A regulator problem for a heat conduction system, of which the eigenstructure is just partially known, is formulated to design a stabilizing controller that keeps a performance index less than a prescribed value. The index is made of the spatio integral of the squared deviation from reference temperature distribution. It is shown that through characterizing frequency response from input to temperature at each spatial point, a distributed parameter system with nominal model and additive uncertainty weight, both of which are real rational, is reconstructed using knowledge of the eigenstructure. A main result claims that the formulated problem is reduced to a standard mixed H 2 / H ∞ one for a linear finite dimensional time-invariant system. Numerical study demonstrates feasibility of the proposed design scheme.

In this paper we present finite-dimensional modeling of heat conduction systems with frequency and space-dependent error bounds where the eigenstruc-tures of the systems are only partially known. It is shown that steady state analysis for dc input of a system is used effectively in reduced order modeling and bounding errors for the whole spatial distribution of temperature. A class of nominal models is clarified with tight additive error bounds, and the nominal models as well as the uncertainty weights are explicitly described as simple real rational form of transfer functions. The feasibility of the presented scheme is demonstrated by a simple example of heat conduction in copper rod.

In this paper, we propose a decentralized scheduling method for flowshop scheduling problems with resource constraints using the Lagrangian decomposition and coordination approach. When a flowshop scheduling problem with resource constraints is decomposed into machine-level subproblems, the decomposed problem becomes very difficult to solve so as to obtain the optimal solution, even when the production sequence of operations is given. In this study, the decomposed subproblems are solved by a simulated annealing algorithm combined with dynamic programming. By decomposing the problem into single machine subproblems, the changeover cost can easily be incorporated in the objective function. In order to reduce the computation time, a heuristic algorithm for calculating the starting times of operations is also proposed. The performance of the proposed method is compared with that of the simulated annealing method by which the schedule of the entire machine is successively improved. Numerical results have shown that the proposed method can generate better solutions than the conventional method. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 149(1): 44–51, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10364

In the multi-vehicle transportation problem, a well-known combinatorial optimization problem, flexibility and adaptability to new situations is required when faced with things such as traffic jams, new customers, or economical vehicle allocation. In this paper, we propose a decentralized probabilistic algorithm, designed using a multi-agent system, in which agents autonomously and independently search for the partial solution to the problem, their customers, and traveling routes to minimize transportation costs under some constraints. The iterative improvement method is as follows. Customers are initially allocated to agents by random generation. An initial solution then is composed by the nearest neighbor algorithm. One customer belonging to an agent is selected randomly, and the customer belonging to the other agent is allocated to the agent with transition probabilities inversely proportional to the 2-dimensional Euclidean distances between the selected customer and the others. The new candidate partia...

In most of Light Emitting Diode (LED) semiconductor fabricating line, AGVs (Automated Guided Vehicles) are used to transport the products between the facilities in order to ensure dust-free operation in the transportation systems. It has been demanded to realize the route planning system which speedily derives a feasible transportation route so that each AGV does not interfere with other AGVs. In this paper, we applied an agent-based optimization method for the route planning problems with multiple AGVs in LED fabricating bay in which the total transportation time is minimized. In the agent-based method, each AGV agent independently searches its candidate of route. The imitated pheromone is effectively used to generate the route which avoids the collision among AGVs. In order to evaluate the performance of the proposed method, the lower bound of the original problem is obtained by using the Lagrangian relaxation technique. The performances of the proposed method are evaluated by using the value of duality gap for a large scale problem. The effectiveness of the agent-based optimization method is demonstrated.