Unexpected Accidents Research Articles

Oxidative decomposition of ammonium ion with ozone in the presence of cobalt and chloride ions for the treatment of radioactive liquid waste

To prevent unexpected accidents at nuclear facilities caused by accumulated ammonium nitrate in an aqueous liquid waste containing ammonium salts and nitric acid, NH4+ in the liquid waste must be decomposed under mild reaction conditions. In this study, we investigated the oxidative decomposition of NH4+ with O3 at 333 K in the presence of a homogeneous Co2+ catalyst and Cl− in the wide pH range of the test solution. The reaction behavior was greatly affected by pH of the test solution. In a basic solution at pH 12, high conversion of NH4+ was obtained even in the absence of Co2+ and Cl−, and the main product was NO3−. However, Co2+ and Cl− in the solution greatly enhanced the decomposition rate of NH4+ in acidic to mild basic solutions (pH 1–8), while only low conversion of NH4+ was observed unless both Co2+ and Cl− were present. For the reaction with Co2+ and Cl− in the solutions, NH4+ was transformed mainly into chloramines (NHxCl3−x, x = 1–3) by the reaction with HClO, which was formed by the reaction of Cl− with O3 catalyzed by the homogeneous Co2+ catalyst, and led to the high decomposition rate of NH4+. Cl− suppressed the formation of the precipitate CoO(OH) during the reaction and consequently the Co2+ catalyst stably existed in the reaction solution, which was another reason for the high decomposition rate of NH4+ in the presence of Cl−. Owing to the swift decomposition of NH4+ under mild reaction conditions and small formation of secondary waste, the oxidative decomposition of NH4+ in the presence of the homogeneous Co2+ catalyst and Cl− is suitable and applicable for the treatment of the aqueous liquid waste containing ammonium salts and nitric acid.

Comparisons between passive RCCSs on degree of passive safety features against accidental conditions and methodology to determine structural thickness of scaled-down heat removal test facilities

Reactor Cavity Cooling Systems (RCCSs) with passive safety features use the atmosphere as a coolant, which cannot be lost; however, the drawback is that they are easily affected by atmospheric disturbances. To realize the commercial application of two types of passive RCCSs, their safety must be evaluated, i.e., they must be able to remove heat from the reactor at all times and under any conditions, including both expected and unexpected natural phenomena and accidents. The objectives of this study are to understand the characteristics and the degree of passive safety features for heat removal by comparing RCCSs based on atmospheric radiation and on atmospheric natural circulation under the same conditions. Simulations of accidental conditions, such as increasing average heat transfer coefficient via natural convection due to natural disasters, were performed using STAR-CCM+, and a methodology to control the amount of heat removal was discussed. A new RCCS based on atmospheric radiation is recommended because of the excellent degree of passive safety features/conditions, and the ability to control amount of heat removal by heat transfer surfaces. Finally, a methodology to determine the structural thickness of RCCS of scaled-down heat removal test facilities for reproducing natural convection and radiation was developed, and experimental methods using pressurized and decompressed chambers were proposed. The authors are convinced that the proposed RCCS based on atmospheric radiation has advantage that the temperature of the RPV can be maintained stably against the disturbance of outside air.

A Research on V2I-based Accident Prevention System for the Prevention of Unexpected Accident of Autonomous Vehicle

A Research on V2I-based Accident Prevention System for the Prevention of Unexpected Accident of Autonomous Vehicle

Comprehensive Assessment of Spatial Distribution of Road-Network Vulnerability in South Asia

Unexpected phenomena-natural disasters, accidents and wars-can disrupt regional traffic systems because of the incapacities of road networks to sustain inflicted impacts. Therefore, the assessment of road-network vulnerability becomes integral to traffic-safety-maintenance and accident-prevention procedures. This study demonstrates the development of a comprehensive network system based on suggested improvements in node extraction, road construction and network attribution. An indicator system for assessing road-network vulnerability was established to be compatible with the South Asian environment and its geographical characteristics by integrating multisource data-topography, geology and meteorological and geopolitical environments. Employing the GIS-based spatial-analysis technique, a vulnerability-assessment model for South Asian road networks was constructed and spatial distributions of vulnerability characteristics were examined under extreme natural and human-influenced environments. The results demonstrate that the road-network-vulnerability distribution in India follows a decreasing trend outwards from the central part of the country. The highest road-network-vulnerability levels of 7 and 8 were observed in the central and northern parts of the Deccan plateau as well as the western coastal and northern mountainous regions. The northernmost part of Pakistan is characterized by road-vulnerability levels in the range of 9-10. Owing to the heavy influence of India-Pakistan conflicts and terror attacks, road networks in proximity to the India-Pakistan border are assigned the highest vulnerability level of 10. The results of this study demonstrate that changes in and the impact of, the geopolitical environment are primary factors influencing road-network vulnerability in South Asia.

Highly Water Durable Solid Electrolyte Type Ammonia Gas Sensor with Ammonium Lanthanum Niobate As an Auxiliary Sensing Electrode

Ammonia (NH3) gas is one of the toxic gases causing a serious health damage, while NH3 is a useful chemical in industrial field. Therefore, for preventing unexpected accidents caused by inhalation, rapid, continuous, and reliable NH3 detection is greatly required by applying smart gas sensor which can put every NH3 emitting site. Among the various kinds of NH3 gas detection tool, the solid electrolyte type sensor is regarded to be a promising candidate for on time monitoring tool due to its selective and quantitative gas detection, thermal stability, and compactness in size, if both the low-temperature sensor operation and satisfactory water durability are realized.In 2011, we have reported a solid electrolyte type NH3 gas sensor showing high water durability at 230°C by using Al3+ ion conducting (Al0.2Zr0.8)20/19Nb(PO4)3 solid electrolyte and water durable NH4 +-β-gallate auxiliary sensing electrode. However, the operation temperature of this sensor (230°C) at which the high sensitivity without electrical noise was obtained are still high for expecting a long-term sensor operation, and it is required to lower the operation temperature. Because a main reason for preventing the low-temperature operation is high electrical resistance of NH4 +-β-gallate, we can expect to lower the operation temperature by newly designing an auxiliary sensing electrode.In this study, we focused on NH4LaNb2O7 in which NH4 + ions existed stably in the layered structure, similar to the NH4 +-β-gallate solid, and fabricated the highly water durable NH3 gas sensor which can operate even at 180°C, by combining with (Al0.2Zr0.8)20/19Nb(PO4)3 solid electrolyte.The NH4LaNb2O7 solid was obtained by the ion-exchange of RbLaNb2O7, which was synthesized by conventional solid-state reaction, in NH4NO3 eutectic solution. Similar to our previous sensor, the sensor element was devised by the combination of (Al0.2Zr0.8)20/19Nb(PO4)3 solid electrolyte, NH4LaNb2O7 auxiliary sensing electrode, and Al metal reference electrode. The NH3 gas sensing performances were investigated at 180°C in atmospheres whose water content was varied from 0 to 4.2 vol.%.From the thermal analysis of the synthesized NH4LaNb2O7 solid, it was found that NH4 + ions stably existed in (NH4)LaNb2O7 solid up to 200°C. Therefore, we decided the sensor operation temperature at 180°C which is slightly lower than the upper temperature limit of NH4LaNb2O7 solid for holding NH4 + ions in its structure. The present sensor exhibited a stable and continuous sensor response to NH3 gas concentration change with a 90% response time of 6 minutes (Response time includes substitution time of test gas in the gas flow line) at 180°C even under humid atmosphere containing 4.2 vol% H2O. Furthermore, the sensor EMF outputs for each NH3 gas concentration were almost same even for varying humidity from 0 to 4.2 vol%, and the EMF changes by changing NH3 gas concentration obeyed a theoretical Nernst relationship over three months, indicating that the present sensor with NH4LaNb2O7 solid as an auxiliary sensing electrode showed the theoretical response for NH3 gas at 180°C with long term stability even in humid atmosphere.

Wood‐Derived Systems for Sustainable Oil/Water Separation

AbstractSeparating oil and water remains a serious challenge because various oil/water mixtures are generated in large quantities by industrial processes, unexpected accidents, and daily life. Various systems containing materials with specific structures and wettabilities have been investigated as possible solutions to this issue. As a sustainable biological material, wood and its derivatives have many advantageous properties for practical applications. The present review mainly focuses on recent progress in using wood‐derived systems for sustainable oil/water separation. Various types of materials that can be produced from wood are briefly introduced. Strategies for constructing wood‐derived separation materials based on direct use and integration, modification, and carbonization are then highlighted. Next, the use of wood‐derived systems for separating oil/water layered mixtures and oil/water emulsions by filtration and absorption methods are systematically discussed. The review concludes with a brief summary of some of the remaining challenges to be overcome.

권상 작업 중 슬링 파손으로 인한 블록 지상 낙하 충격에 대한 준정적 해석

Recently, shipyards are making many efforts to reduce the number of the mounted blocks by increasing the block size. This is to improve productivity and reduce related costs by minimizing block movement and shortening the building period. However, as the blocks become larger, the weight increases considerably. If the target block has a damage due to an unexpected accident during block lifting, it may seriously cause a problem of the reusability of the block. In this study, a large-sized block of the offshore structure weighing 480 tons was lifting with a total of seven sling belts, and one sling belt was broken while it was moving, resulting in a situation in which a part of the edge of the block collided with the ground. The aim of this paper is to verify the structural integrity of the block that directly collides with the ground in the form of free fall due to the sling breakage. Considering that the hook loads acting on several sling belts holding the block are redistributed when a sling belt is broken, the hook loads were recalculated at the angle just before the sling breakage. These loads were used to check the safety of the sling belts. In addition, FE analysis was performed by calculating the amount of impact from the free fall condition, obtaining the impact area by using Hertz

Prussian Blue Analogues of A2[Fe(CN)6] (A: Cu2+, Co2+, and Ni2+) and Their Composition-Dependent Sorption Performances towards Cs+, Sr2+, and Co2+

Investigation in radioactive contaminant removal from aqueous solutions has been considered essential upon unexpected nuclear accidents. In this report, we have successfully prepared Prussian blue analogues (PBAs) with different substituted cations (A2[Fe(CN)6] (A: Cu2+, Co2+, and Ni2+)). The synthesized PBAs were characterized and employed for the removal of Cs+, Sr2+, and Co2+ as sorption models, which are commonly found in radioactive waste. Sorption examinations reveal that Cu2[Fe(CN)6] has the highest sorption capacity towards Cs+, Sr2+, and Co2+ compared with those of Co2[Fe(CN)6] and Ni2[Fe(CN)6]. This is mainly attributed to the cation-exchange ability of substituted metal within the framework of PBAs. The sorption mechanism is qualitatively and quantitatively supported by infrared spectroscopy (IR) and total reflection X-ray fluorescence spectroscopy analysis (TXRF). In addition, it was found that Cs+ is adsorbed most effectively by PBAs due to the size matching between Cs+ ions and the channel windows of PBAs. These findings are important for the design of sorbents with suitable ion-exchange capacity and selectivity toward targeted radioactive wastes.

A full-optical strain FBG sensor for in-situ monitoring of fatigue stages via tunable DFB laser demodulation

Continuously alternating loads and environmental temperature fluctuations can easily cause material aging and fatigue defects in some in-service infrastructures. In order to improve the load-bearing capacities of these infrastructures and prevent unexpected accidents, a flexible continuous detection system that can evaluate the health of these structural systems is needed. The detection system should have the advantages of integrated miniaturization, low cost, high stability and sensitivity, suitability for engineering applications, etc. A full-optical strain sensor based on tunable laser demodulation is designed in this paper. It uses a home-made control mainboard to monitor a 2425-T3 aluminum alloy during tensile and fatigue tests. The tensile test results indicate that the FBG sensor response correlates well with the strain measured using a strain gauge. Therefore, the load and strain can be both monitored via the FBG sensor response. The fatigue test results demonstrate that the FBG sensor presented is superior to the strain gauge and has better sensitivity and stability for vibration monitoring in complex environments. In this letter, we make full use of the FBG’s advantages including its small size and light weight to implement an integrated, miniaturized FBG sensor design with significant structural health monitoring applications.

Analysis of historical road accident data supporting autonomous vehicle control strategies

It is expected that most accidents occurring due to human mistakes will be eliminated by autonomous vehicles. Their control is based on real-time data obtained from the various sensors, processed by sophisticated algorithms and the operation of actuators. However, it is worth noting that this process flow cannot handle unexpected accident situations like a child running out in front of the vehicle or an unexpectedly slippery road surface. A comprehensive analysis of historical accident data can help to forecast these situations. For example, it is possible to localize areas of the public road network, where the number of accidents related to careless pedestrians or bad road surface conditions is significantly higher than expected. This information can help the control of the autonomous vehicle to prepare for dangerous situations long before the real-time sensors provide any related information. This manuscript presents a data-mining method working on the already existing road accident database records to find the black spots of the road network. As a next step, a further statistical approach is used to find the significant risk factors of these zones, which result can be built into the controlling strategy of self-driven cars to prepare them for these situations to decrease the probability of the potential further incidents. The evaluation part of this paper shows that the robustness of the proposed method is similar to the already existing black spot searching algorithms. However, it provides additional information about the main accident patterns.

Blind source extraction based on EMD and temporal correlation for rolling element bearing fault diagnosis

Purpose Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method. Design/methodology/approach To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method. Findings Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle. Originality/value This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.

Improving the Accuracy of Biosignal Analysis Using BCG by Applying a Signal-to-Noise Ratio and Similarity-Based Channel Selection Algorithm

As the socio-economic environment has changed, the number of single and elderly households has increased, and the demand for maintaining a long and healthy life has been increased by continuously monitoring and managing physical conditions against unexpected accidents that may occur when living alone at home. In response to these demands, it is necessary to develop services that provide personalized healthcare services by recording and analyzing living patterns and biometric information in an unconscious way. In this paper, we propose an accuracy improvement method using similarity and Signal-to-noise ratio analysis for BCG measurement method using piezo sensors and a system for continuous personal health monitoring in home without user awareness. Heart rate and respiration rate were derived using the acquired BCG data, and the biometric information is stored symmetrically for personal information security.

Circling Back

Circling Back David Gardiner our twenty-fifth anniversary is a celebration—a celebration of twenty-five years of the Center for Irish Studies, New Hibernia Review, and the works of the many who have shaped the center’s efforts. My first thought was to select the best articles from the previous one hundred issues. I would simply mine databases like Project MUSE, identify the most popular articles, and start from there. As with many ideas, this was a bad one. By the time I was done looking over some of the most downloaded articles, I had also delved into the most important. This resulted in a manuscript of 1,038 pages. Not wishing to publish a trilogy, I realized a more appropriate approach would be to publish our interviews, the actual voices that have spoken out of New Hibernia Review over the past twenty-five years. This collection is not nearly representative of Irish Studies throughout the globe, but each conversation is of great interest, and together they offer an accurate impression of the journal’s work. In celebration of this anniversary, the indomitable Judy Gilats has undertaken a complete redesign of the journal—and, thankfully, the result is not more than a thousand pages long. We hope you enjoy these voices of Irish Studies, past and present. ________ The University of St. Thomas has been a part of my life for a long time. Irish Studies, thanks to Thomas Dillon Redshaw and James Silas Rogers, has shaped my experience. Years on, both of my daughters attended the university, so it’s also become a family affair. For me, though, this connection has been an accident—a very happy and very unexpected accident. I was lucky enough to be fully scholarshipped from my first year at university [End Page 5] through my PhD. That’s where St. Thomas comes in. I was a basketball player on scholarship at the University of Chicago, that powerhouse athletic institution. (U of C did indeed found the Big Ten, and Jay Berwanger, the first Heisman Trophy winner, in 1935, and scourge of Gerald Ford, was a frequent visitor to my fraternity house.) After two knee operations and one ankle reconstruction and with a vertical leap heading quickly south, my “academic” scholarship was in danger, and my tenured coach at U of C called the College of St. Thomas to arrange for a transfer. Fortunately, the St. Thomas basketball coach was connected with the admissions office. (I’m still not sure that I was actually accepted to the college.) I was quite excited since the scholarship was guaranteed and I’d be living in the Virgin Islands. Or so I thought. As it turns out, the College of St. Thomas, now the University of St. Thomas, is as far away from the island of St. Thomas as one may get. St. Thomas at that time was very similar to the University of Chicago. It had buildings and professors and classes. More importantly, I couldn’t lose my scholarship in spite of losing my ability to play basketball. There was one mysterious and enigmatic professor whom I was continually steered toward. He was and is a true scholar and educator and, I’m grateful and honored to say, now a friend. Tom Redshaw was my connection and introduction to John Montague, Tom Kinsella, and Seán Lucy, and through them to some of my best friends in Cork, Moyclare Park, Galway, and Belfast. As do Éire-Ireland and New Hibernia Review, he opened up a world to me and to others. This expansive connectedness is very evident in his memoir that follows. As an undergraduate, I barely saw this happening. In more than one English class, a professor might be unable to answer my question and would say, “you need to ask Dr. Redshaw about that.” This Redshaw character was something between Gandalf and the Caterpillar in Alice in Wonderland. He remains so. It was in Redshaw’s office that I learned how to learn. To this day I love Tom’s directness because it is a laser into the life of the mind. In his office, I’d look at his shelves filled with Dolmen...

Interruptions: Affective futures and uncanny presences atGiemaš, Finnmark

AbstractThis paper concerns affective relations and unexpected interruptions as the planned expansion of an extractive open-pit mining site gathers momentum. The site is a mountain in Varanger, North Norway, criss-crossed by a sand-coloured meshwork of roads that are part of the current infrastructure of a quartzite quarry. Recently purchased by Chinese investors, the mining company Elkem plans a massive expansion of the operations, which will interrupt a wide range of practices and projects, including the migratory movement of reindeer, as well as their grazing patterns. Known asGiemašamongst Sámi speakers, the mountain is also alluded to as a site of other powers, manifesting as unexpected accidents. In this article, I explore how the planned expansion evokes this contested site as more than a singular mountain, and how divergent epistemic formations interrupt the making of extractive resources in multiple ways.

Effect and mechanism of C2HF5 on premixed flame of runaway gas (syngas) in lithium-ion batteries

Lithium-ion battery is a new type of clean energy. Due to manufacturing defects, improper use, unexpected accidents and other reasons, lithium-ion battery thermal runaway fire and explosion accidents are frequently re-ported, and its safety has become an important issue to be solved. In this paper, the effect of second-generation halon substitute C2HF5 on runaway gas premix flame of lithium-ion battery are studied by experiments and numerical simulations.The bunsen burner was used to measure the changing trend of the premixed flame velocity of the runaway gas/air under different equivalent ratios and different concentrations of C2HF5. The results show that the flame velocity is in good consistency with the model under the conditions of lean flame and low inhibitor addition.The promotion effect of inhibitors on combustion has been verified by bunsen flame. Compared with CH4, C2HF5 is more suitable for inhibiting syngas flame of lithium-ion batteries, and the effect on the rich flames is better than lean flames.

A Cooperative Autonomous Scheduling Approach for Multiple Earth Observation Satellites With Intensive Missions

Autonomous mission scheduling of multiple earth observation satellites (multi-EOSs) is considered as a complicated combinatorial optimization problem, which requires simultaneous consideration of imaging needs, resource constraints (electricity and memory) and possible emergencies. However, EOS resources are extremely scarce relative to intensive mission observation demands and most of the existing algorithms seldom consider emergencies. To address these challenges, this paper proposes a complete multi-EOSs scheduling scheme composed of two coupling stages, including mission pre-planning and mission replanning. We aim to obtain the optimal scheduling scheme for each EOS at the same time by maximizing the observation profits and balancing the resource consumption of each EOS. In this study, the roles of solar energy and ground stations in multi-EOSs mission scheduling are also considered. In the first stage, based on the cooperation and competition mechanism as well as the dynamic adjustment approach, an evolutionary ant colony optimization (EACO) method is developed to obtain the optimal solution for multi-EOSs pre-planning. In the second stage, using the results produced by EACO, we propose an interactive replanning approach to replan the missions that cannot be performed by faulty EOS in the event of unexpected accidents. Finally, several target scenarios are designed and numerical experiments are performed to show that the proposed algorithm presents better performance for large-scale multi-EOSs missions than other state-of-the-art algorithms.

Research on Health Management Technology and Application of Lifting Machinery

Lifting machinery is a key facility for the development of modern industry. It’s not noly an important support for the construction of major national projects but also one of the core competitiveness of my country’s equipment manufacturing industry. With the development of large-scale, high-parameterization and intelligentization of lifting appliances, technologies such as safety monitoring and health monitoring have been gradually applied to industrial fields such as ports and metallurgy. In order to further develop the data-driven health management technology and engineering application of cranes, on the basis of elaborating the health management needs of cranes, the current status and development trends at home and abroad, we explored and proposed a technical framework for health management of cranes, and analyzed the lifting Key technologies such as machinery safety monitoring, health monitoring, health status diagnosis, failure trend prediction, and predictive maintenance decisions. Finally combined with a 200t casting crane in a steel plant, carried out safety monitoring and data-driven diagnosis, prediction and system visualization management. The results show that: the hoisting machinery health management system can monitor and monitor in real time and carry out early warning and forecasting to avoid the occurrence of unexpected accidents; based on predictive maintenance decisions, it helps enterprises to reasonably arrange maintenance cycles and maintenance methods to reduce the maintenance cost of the enterprise; reduce hoisting machinery downtime Time to enhance the effective value of assets; and enhance the level of enterprise information management.

Similarity-Measured Isolation Forest: Anomaly Detection Method for Machine Monitoring Data

A rough environment or unexpected accident of data acquisition instrument can introduce some anomalies in monitoring data. Those anomalies reduce data quality and lead to the incorrect recognition of machine health status. However, the research on anomaly detection of machine monitoring data (MMD) is very scarce. Moreover, anomaly detection methods in other fields cannot be directly applied to MMD. Therefore, a robust anomaly detection method called similarity-measured isolation forest (SM-iForest) is proposed to detect abnormal segments and the data therein. The inadaptability and instability of iForest were reduced while processing MMD benefiting from the characteristics of sliding-window processing. Moreover, an anomaly identification stage measuring the relative similarity of possible abnormal segments further improved the robustness of iForest. The effectiveness of the proposed method was verified with a vibration simulation signal and three sets of milling force signals. The results demonstrate that SM-iForest can detect the missing, shifting, and swelling segments robustly. Detection results of comparing seven methods suggest that SM-iForest is a promising method to detect MMD anomaly with a high detection rate and low false alarm rate.

Controller Resynthesis for Multirobot System When Changes Happen

The synthesis problem is solved by constructing a system that fulfills given requirements when interacting with the environment, one of the most crucial and challenging tasks in robotics. However, unexpected accidents will cause the synthesized controllers of the multirobot system to be invalidated.