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This study deals with the estimation of the changes that occur in the Business-to-Business (B2B) networks in the Japanese textile and apparel industry by applying datasets of about 2000 companies from 2011/2012 to 2015/2016. Network analysis was used to examine the properties of the B2B networks. A factor of innovation in information and communications technology (ICT) and logistics technology was introduced into an agent-based model to demonstrate changes occurring in the related structures of B2B networks. The agent-based model was designed and tested based on qualitative information on Japanese textile and apparel industries. Consequently, network analysis revealed power-law properties and the structures of centralized hub companies. Moreover, in the simulation experiments, the centralizations of the networks generated by the agent-based model due to innovation in ICT and logistics technology were illustrated. Therefore, one of the predicted cases regarding changes that occur in the B2B networks was explained as centralizations to hub companies.

Swarm robotic systems (SRSs) are a type of multi-robot system in which robots operate without any form of centralized control. The typical design methodology for SRSs comprises a behavior-based approach, where the desired collective behavior is obtained manually by designing the behavior of individual robots in advance. In contrast, in an automatic design approach, a certain general methodology is adopted. This paper presents a deep reinforcement learning approach for collective behavior acquisition of SRSs. The swarm robots are expected to collect information in parallel and share their experience for accelerating their learning. We conducted real swarm robot experiments and evaluated the learning performance of the swarm in a scenario where the robots consecutively traveled between two landmarks.

Redundancy in the number of robots is a fundamental feature of robotic swarms to confer robustness, flexibility, and scalability. However, robots tend to interfere with each other in a case, where multiple robots gather in a spatially limited environment. The aim of this paper is to understand how a robotic swarm develops an effective strategy to manage congestion. The controllers of the robots are obtained by an evolutionary robotics approach. The strategy of managing congestion is observed in the process of generating a collective path of robots visiting two landmarks alternately. The robotic swarm exhibits autonomous specialization that the robots traveling inside the path activate the LEDs, while the robots in the outer side deactivate them. We found that the congestion is regulated in an emergent way of autonomous specialization by the result of an artificial evolution.

Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

Swarm robotics is a field in which multiple robots coordinate their collective behavior autonomously to accomplish a given task without any form of centralized control. In swarm robotics, task allocation refers to the behavior resulting in robots being dynamically distributed over different sub-tasks, which is often required for solving complex tasks. It has been well recognized that evolutionary robotics is a promising approach to the development of collective behaviors for robotic swarms. However, the artificial evolution often suffers from two issues—the bootstrapping problem and deception—especially when the underlying task is profoundly complex. In this study, we propose a two-step scheme consisting of task partitioning and autonomous task allocation to overcome these difficulties. We conduct computer simulation experiments where robotic swarms have to accomplish a complex collective foraging problem, and the results show that the proposed approach leads to perform more effectively than a conventional evolutionary robotics approach.

Swarm robotic systems (SRS) are a type of multi-robot systems that consist of many homogeneous autonomous robots inspired by social insects. In our pervious study, we succeeded in developing end-to-end control policies for SRS using Deep Q-Network (DQN) algorithm. However, since DQN is totally a black box, it is difficult to understand what were learnt through the learning process. Therefore, in this paper, a novel method of visualizing the decision making process in the DQN is proposed by combining Deconvolutional Network (Deconvnet) and Gradient-weighted Class Activation Mapping (Grad-CAM). Then we show what are being preserved as the deep features and which part of input image is concerned to make an action decision. The proposed method is demonstrated by conducting the computer simulations of a round trip task, in which the swarm robots need to visit two different locations alternatively as many times as possible. The computer simulations might also be explained that the proposed method visualizes the policies learned by DQN.

This paper focuses on the collective cognition of robotic swarms. The robotic swarms perform tasks that are beyond the capability of a single robot by collective behavior that emerge from local interactions. However, due to the lack of the capability of a single robot, it is necessary for robotic swarms to collectively perceive the environment. In this paper, we develop controllers for a robotic swarm to accomplish a complex cognitive task, namely the collective foraging task with poison. In this task, robots have to both collectively distinguish two objects, namely foods and poisons, and cooperatively transport foods to the nest. We applied an evolutionary robotics approach with covariance matrix adaptation evolution strategy to develop controllers for robotic swarms. The results of computer simulations show that collective cognition behavior was successfully generated, which allows the robots to transport only foods. In addition, we also perform experiments to examine the scalability of the developed controllers.

The Traveling salesman problem (TSP) defines the problem of finding the optimal path between multiple points, connected by paths of a certain cost. This paper applies that problem formulation in the maritime environment, specifically a path planning problem for a tour boat visiting popular tourist locations in Medulin, Croatia. The problem is solved using two evolutionary computing methods – the genetic algorithm (GA) and the simulated annealing (SA) - and comparing the results (are compared) by an extensive search of the solution space. The results show that evolutionary computing algorithms provide comparable results to an extensive search in a shorter amount of time, with SA providing better results of the two.

In this study, the use of a popular deep reinforcement learning algorithm – deep Q-learning – in developing end-to-end control policies for robotic swarms is explored. Robots only have limited local sensory capabilities; however, in a swarm, they can accomplish collective tasks beyond the capability of a single robot. Compared with most automatic design approaches proposed so far, which belong to the field of evolutionary robotics, deep reinforcement learning techniques provide two advantages: (i) they enable researchers to develop control policies in an end-to-end fashion; and (ii) they require fewer computation resources, especially when the control policy to be developed has a large parameter space. The proposed approach is evaluated in a round-trip task, where the robots are required to travel between two destinations as much as possible. Simulation results show that the proposed approach can learn control policies directly from high-dimensional raw camera pixel inputs for robotic swarms.

Self-organized flocking of robotic swarms has been investigated for approximately 20 years. Most studies are based on a computer animation model named Boid. This model reproduces flocking motion by three simple behavioral rules: collision avoidance, velocity matching, and flock centering. However, flocking performance depends on how these rules are configured and no guideline for the configuration exists. This paper investigates real robot flocking where individuals can switch their roles depending on the situations. Robots can move as leaders or followers, and the roles are dynamically allocated using stochastic learning automata. The flocking performance is evaluated, and swarming behavior is analyzed in a scenario where robots consecutively travel between two landmarks.

Swarm robotic systems are a type of multi-robot systems, in which robots operate without any form of centralized control. The most popular approach for SRS is the so-called ad hoc or behavior-based approach; desired collective behavior is obtained by manually by designing the behavior of individual robot in advance. On the other hand, in the principled or automatic design approach, a certain general methodology for developing appropriate collective behavior is adopted. This paper investigates a deep reinforcement learning approach to collective behavior acquisition of swarm robotics systems. Robots are expected to collect information in parallel and share their experience for accelerating the learning. We conduct real swarm robot experiments and evaluate the learning performance in a scenario where robots consecutively travel between two landmarks.

Swarm robotics is a field of research in which multiple robots coordinate behaviors to solve cognitive problems that exceed individual ability. This paper reports a case study of swarm robotics in a collective cognition task where robots have to cooperatively forage for foods of specific size and bring them back to the nest. The sensory ability of each robot is limited to local and is insufficient to distinguish food's size. We succeeded in developing an artificial neural network controller through the use of evolutionary swarm robotics approach. Computer simulation results show that the robotic swarm exhibits collective cognition to achieve the given task.

A 71-year-old woman was diagnosed with sick sinus syndrome (SSS) upon her cardiogenic cerebral embolism and underwent pacemaker implantation. Active fixation leads were positioned at the right atrial appendage and ventricular septum. Twenty-one days later at her routine checkup, she was asymptomatic and there were no signs of cardiac tamponade or pacing failure. But echocardiography and computed tomography revealed a large amount of pericardial effusion due to the lead perforation. We performed open drainage because her anticoagulant could not be stopped for her history of cerebral infarction. Upon surgery, 400 ml of hemorrhagic pericardial effusion was drained and we found a lead tip penetrating through the right atrial appendage. We should carefully observe for lead perforation after pacemaker implantation, especially when using the active fixation lead.

To prevent haemodynamic stroke during cardiovascular surgery in patients with carotid stenosis, we routinely evaluated magnetic resonance angiography and selectively evaluated brain perfusion single-photon emission computed tomography with acetazolamide challenge. Off-pump surgery was preferred when cerebral blood flow reserve was impaired. This strategy's usefulness was investigated.Among the 1059 consecutive patients who underwent preoperative carotid screening by magnetic resonance angiography, 84 (7.9%) patients had50% stenosis; 45 of them underwent brain single-photon emission computed tomography. The severity of cerebral blood flow compromise was estimated by the proportion of Stage 2 area in the affected territory, in which both resting blood flow (32 ml/min) and flow reserve (10%) were reduced.Perioperative stroke occurred in 1.7% overall (18/1059), in 6% (5/84) of those with carotid stenosis and in 1.3% (13/975) of those without stenosis (P = 0.010). On subgroup analysis, carotid stenosis was associated with an increased risk of stroke in the on-pump surgery group [n = 949, 5/59 (9%) with stenosis vs 11/890 (1.1%) without stenosis, P = 0.002], while it was not in the off-pump group [n = 110, 0/25 (0%) with stenosis vs 2/85 (2%) without stenosis, P = 0.59]. With respect to the role of acetazolamide single-photon emission computed tomography, 2 of the 4 patients with Stage 2 area10% undergoing on-pump surgery without preceding carotid revascularization developed stroke, while none of the 21 patients with Stage 2 area10% undergoing on-pump surgery developed stroke (P = 0.020).Carotid stenosis is a risk factor for perioperative stroke in on-pump surgery. Patients with large Stage 2 area (10%) are at increased risk of perioperative stroke when on-pump surgery is performed.

OBJECTIVES: Specificity of transcranial motor-evoked potentials (MEPs) is low because amplitude fluctuation is common, which seems due to several technical and fundamental reasons including difficulty in electrodes positioning and fixation for transcranial stimulation and susceptibility to anaesthesia. This study aimed to investigate the feasibility, safety and stability of our novel technique of transoesophageal spinal cord stimulation to improve the stability of MEPs. METHODS: Ten anaesthetized adult beagle dogs were used. Transoesophageal stimulation was performed between the oesophageal luminal surface electrode (cathode) and a subcutaneous needle electrode (anode) at the fourth to fifth thoracic vertebra level. Stimulation was achieved with a train of five pulses delivered at 2.0-ms intervals. Compound muscle action potentials were recorded from four limbs and external anal sphincter muscles. Stability to anaesthetic agents was tested at varying speeds of propofol and remifentanil, and effects of varying concentration of sevoflurane inhalation were also evaluated. RESULTS: Transoesophageal MEPs could be recorded without difficulty in all dogs. Fluoroscopic evaluation showed that electrodes misalignment up to 5 cm cranially or caudally could be tolerated. Stimulus intensity to achieve maximum amplitude of hindlimb muscle potentials on both sides was significantly lower by transoesophageal stimulation than by transcranial stimulation (383 ± 41 vs 533 ± 121 V, P= 0.02) and had less interindividual variability. Latency of transoesophageal MEPs was shorter than that of transcranial MEPs at every recording point. No arrhythmia was provoked during stimulation. Animals that were allowed to recover showed no neurological abnormality. In the two sacrificed animals, the explanted oesophagus showed no mucosal injury. Stability to varying dose of anaesthetic agents was similar between transoesophageal and transcranial stimulation, except for the potentials of forelimbs by transoesophageal stimulation that were resistant to anaesthetic depression. CONCLUSIONS: Transoesophageal stimulation for MEPs monitoring was feasible without difficulty and safe. Although its stability to anaesthetic agents was similar to that of transcranial stimulation, its technical ease and small interindividual variability warrants further studies on the response to spinal cord ischaemia.

OBJECTIVES: Arterial variation is common in the vertebral artery, and simple occlusion of the left subclavian artery may result in brain infarction, especially when it terminates in the posterior inferior cerebellar artery (PICA). We report the results of preoperative vertebral artery evaluation by magnetic resonance angiography (MRA) and its impact upon operative strategy. METHODS: Among the 214 patients who underwent thoracic aortic surgery from 2009 through 2012, 159 patients with preoperative MRA were retrospectively analysed. Patients’ age ranged from 35 to 88 (median 72), 122 were male and 115 had degenerative aneurysms. Prevalence rates of vertebral artery variations and occlusive lesions were reported, together with operative strategies and outcomes. RESULTS: There were 19 hypoplasia (12%), 10 PICA termination (6%) and 12 occlusive lesion (8%) on the right vertebral artery and 10 hypoplasia (6%), 5 PICA termination (3%), 7 direct arch origin (4%) and 3 occlusive lesion (2%) on the left. Two of the seven arch-originated arteries terminated in the PICA. In aortic arch replacement, these were reconstructed together with the left subclavian artery while hypothermia was maintained. During thoracic endovascular aortic repair with Zone-2 proximal landing, debranching bypass was employed to preserve left subclavian perfusion when there was PICA termination, hypoplasia or occlusive lesion. In 1 patient with hypoplasia between the basilar artery and the left PICA, bypass was added immediately after deployment because radial pressure dropped critically. No brain infarction occurred with this strategy. CONCLUSIONS: PICA termination and right side hypoplasia/occlusive lesion, where left subclavian perfusion is important for brain protection, is present in 30%. Left vertebral artery that originated from the arch should be managed with care, because PICA termination is highly prevalent.

We successfully treated small saccular abdominal aortic aneurysms involving the renal artery origin with direct sagittal suture closure under supra-celiac or supra-superior mesenteric artery cross clamping after renal artery reconstruction in two cases. This technique might be a useful option for localized saccular aortic aneurysms in selected cases.

Swarm robotic systems consist of many homogeneous autonomous robots with no type of global controllers. It is difficult to design controllers for such behavioral systems, since the behavior of system level is the emergent results of the dynamical interaction between the systems and the environment. This paper uses a method that in which robot controllers are designed by a covariance matrix adaptation evolution strategy (CMA-ES) with artificial neural networks. This approach is referred to as CMA-NeuroES. Among the many evolutionary algorithms for evolving artificial neural networks, two conventionally representation approaches, fast evolution strategies and differential evolution, are used for comparison. The cooperative transport problem is used as a benchmark of swarm robotic systems to test their performance. Results show that CMA-NeuroES has the overall best performance of the three.

This study focuses on business relationships in Japan for industries pertaining to dyeing and finishing. The authors analyzed the complex network of 3354 companies contained in the Annual Report on the textile-related trade, 1998 edition published by Sen-I Sha Company. Based on community extraction proposed by Newman in 2004, there are 46 communities in complex network and 2 large competitive communities containing a lot of dyeing and finishing companies are existed. As results of community extraction of the complex network that deletes one big company on the betweenness centrality, the structure of the community is changed.

Swarm robotic systems (SRS) are a type of multi-robot systems, in which robots operate without any form of centralized control. In SRS, the generation of a complex swarm behavior resulting in robots being dynamically distributed over different sub-tasks requires an autonomous task allocation mechanism. It has been well recognized that evolutionary robotics with an evolving artificial neural network is a promising approach for generating collective swarm behavior. However, the artificial evolution often suffers from the bootstrap problem, especially when the underlying task is very complex. On the other hand, the behavioral decomposition, which is based on the divide-and-conquer thinking, has been reported to be effective for overcoming the bootstrap problem. In this paper, we describe how a behavioral decomposition based evolutionary robotics approach can be applied to synthesize a composite artificial neural network based controller for a complex task. The simulation results show the hierarchical strategy based evolutionary robotics approach is effective for generating autonomous task allocation behavior for a swarm robotic system.

Learning autonomous robots have been widely discussed in recent years. Reinforcement learning (RL) is a popular method in this domain. However, its performance is quite sensitive to the segmentation of state and action spaces. To overcome this problem, we developed the new technique Bayesian-discriminationfunction-based RL (BRL). BRL has proven to be more effective than other standard RL algorithms in dealing withmulti-robot system(MRS) problems. However, as in most learning systems, occasional overfitting problems occur in BRL. This paper introduces an extended BRL for improving the robustness of MRSs. Metalearning based on the information entropy of fired rules is adopted for adaptive modification of its learning parameters. Computer simulations are conducted to verify the effectiveness of our proposed method.

Life expectancy of the chronic dialysis patients depends upon the underlying renal disease, but its influence on the outcome of heart valve replacement has not been studied. We aimed to elucidate the difference in the early and midterm results of heart valve replacement according to the etiology of renal diseases. We retrospectively analyzed 17 patients on chronic dialysis who underwent heart valve replacement from 2002 to October 2009. Underlying renal disease was primary in ten patients (glomerulonephritis 8, others 2) and secondary in seven (nephrosclerosis 4, diabetic nephropathy 3). Mean age was 61 ± 10 for primary and 67 ± 7 for secondary renal diseases. Mean duration of dialysis was 18 ± 6 years for primary and 9 ± 9 for secondary renal diseases (p = 0.02). In the aortic position, mechanical valves were used in 12 patients and bioprostheses in three. In the mitral position, mechanical valves were used in all four. Deep hypothermic operation was required for severe aortic calcification in five (primary 5). Mean follow up period was 23 ± 18 months. There were two in-hospital deaths in patients with primary renal disease. Three-year survival rate including hospital deaths, on the other hand, was higher for primary renal diseases (80%) than secondary ones (34%). Despite elevated perioperative risk due to consequence of longer duration of dialysis, midterm survival of patients with primary renal diseases seemed better than for those with secondary renal diseases. These results may help the choice of heart valve prosthesis in chronic dialysis patients.

Swarm robotics is the research field of multi-robot systems which consist of many homogeneous autonomous robots without any type of global controllers. Generally, since a task given to this system can not be achieved by a single autonomous robot, emergent cooperative behavior is expected in a robot swarm by a certain mechanism through the interactions among the robots or with an environment. In this paper, an evolutionary robotics approach, i.e., the method that the robot controllers are designed by evolutionary algorithms with artificial neural networks, is applied. Among many approaches to evolving artificial neural networks, two approaches, NEAT and MBEANN, are adopted for computer simulations. Although a conventional neural network evolves only synaptic weights with the condition of a fixed topology, NEAT and MBEANN evolve not only their synaptic weights but also their topologies. As a benchmark of swarm robotics, cooperative package pushing problems by ten autonomous robots are conducted to examine their performance. The emerged behavioral characteristics are discussed.

Fitness landscapes which include neutrality have been conceptualized as containing neutral networks. Since the introduction of this concept, EC researchers have expected that a population can move along neutral networks without getting trapped on local optima. On the other hand, it has been demonstrated in tunably neutral NK landscapes that neutrality does not affect the ruggedness, although it does reduce the number of local optima. These show that the effects of neutrality are still contentious issues. This paper investigates the effects of neutrality and ruggedness on structures of fitness landscapes. A neutral network is described in a mathematical form based on Harvey's original definition with minor modifications. According to this description, genotypic search space in a problem which includes both neutrality and ruggedness can be classified into several neutral networks. These structures in and between neutral networks are then analyzed. Our results demonstrate that landscapes with a higher degree of neutrality have the larger sizes of neutral networks. For landscapes with the smallest degree of ruggedness, all neutral networks have some contact points to the networks of higher fitness. For landscapes with a higher degree of ruggedness, there are few contact points between the networks of high fitness and the ones of the highest fitness, which seem to be isolated, deceptive or rugged.

The paper proposes an extended method for improving robustness of reinforcement learning called BRL. BRL has a novel character that the continuous state space and the continuous action space are segmented autonomously and simultaneously in the online-learning process. We have presented elsewhere that BRL is an effective technique not only for single robot problems but also for multi-robot problems. In BRL, the continuous state space is segmented by the Bayesian discrimination function method based on the instances perceived from each episode. On the other hand, the continuous action space is segmented by the same method based on randomly generated actions. This seems reasonable when a perceived state is apparently different from the states in the acquired rules. But it seems inappropriate when a perceived state is somewhat similar to the states in the acquired rules. Therefore, in the latter case, we propose an extension of BRL such that an action is calculated as the weighted linear interpolation of the actions in the similar rules. After showing the formalization of the proposed extension, the navigation problem of an autonomous mobile robot is demonstrated to verify the improvement by the proposed method through computer simulation as well as physical experiments.

Neutral networks, which occur in fitness landscapes containing neighboring points of equal fitness, have attracted much research interest in recent years. In our recent papers, we have shown that, in the case of simple test functions, the mutation rate of a genetic algorithm is an important factor for improving the speed at which a population moves along a neutral network. Our results also suggested that the benefits of the variable mutation rate strategy used by the operon-GA increase as the ruggedness of the landscapes increases. In this paper, we conducted a series of computer simulations with evolutionary robotics (ER) problems in order to investigate whether our previous results are applicable to this problem domain. The evolutionary dynamics we observed were consistent with those observed in our previous experiments, confirming that the variable mutation rate strategy is also beneficial to the ER problems.

本稿ではセル生産における自律分散的な作業現場において，作業者が持つ作業機械に関する情報について作業者機械評価表を用いて簡便に抽出することを試みる．そして，作業現場がフラットな組織に近く，リーダーが存在しない場合に，PM理論に基づく一定のリーダーシップを持つインフォーマル・リーダーを選出し，ナレッジ・マネジメントを促進する簡潔な方策を提案する．そして，実際のクリーニング工場のセル生産現場において，PM理論に基づくインフォーマル・リーダーを中心にして作業者から作業機械情報を抽出し，それに基づくナレッジ・マネジメントによって作業時間を短縮することによって，心理的見地に基づく情報共有の有用性を実証した．