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Background Difficult airway is a clinical situation in which a trained anesthesiologist experiences trouble with facemask ventilation and/or laryngoscopy and/or intubation. Poor identification of at-risk patients has been identified as one of the causes of difficult airway management. Objectives We aimed to review the literature regarding the use of wristbands to identify adult patients with known or predicted difficult airway in hospitals. Methods We searched Web of Science (WoS), Scopus, MEDLINE and OVID following the stages described by the PRISMA Extension for Scoping Reviews (PRISMA-ScR). We used a combination of MeSH terms and non-controlled vocabulary regarding the use of difficult airway wristbands in adults. Three researchers independently reviewed the full texts and selected the papers to be included based on the inclusion criteria. Results Our search generated 334 articles after removing duplicates. After reviewing full text articles, only seven studies were included. Here we found that most were from the United States, in which the authors report the use of in-patients’ wristbands in adults. According to the authors, the use of wristbands is being implemented as a measure of improved quality and safety of in-patients with difficult airway either known or suspected. Conclusions The identification with wristbands of a difficult airway at an appropriate time is an identification strategy can have a low cost but a high impact on morbidity. It is pertinent to develop a methodology such as the use of wristbands, that allows a good classification and identification of patients with difficult airway in hospitals from Latin America.

Performance evaluation of large distributed systems plays a pivotal role in the design of Internet of Things (IoT) applications, or Big Data analysis, where high scalability and low response times are required. However, this performance assessment requires models, methodologies and tools tailored for this type of systems. Product-form queueing networks have been widely adopted for the analysis of sequential computations thanks to the availability of efficient algorithms for the computation of the average performance indices. However, this formalism has strong limitations since it does not allow one to model fork-join constructs or batches of jobs. Product-form stochastic Petri nets partially overcome these limitations but, on the other hand, general algorithms for the computation of the expected performance indices are not known or require strong assumptions on the model structure. In this paper, we define a new algorithm for the analysis of product-form stochastic Petri nets which works under much less restrictive assumptions than those previously proposed. The idea is that, in many cases, the entire state space of the model can be stored in memory thanks to multi-valued decision diagrams and the computation of the net’s performance indices can take advantage of the tree structure that characterises this representation. Finally, we present a case study and several examples that will be used to study the performance of the algorithm for realistic scenarios.

Familial Parkinson disease (PD) is associated with rare genetic mutations, but the etiology in most patients with sporadic (s)PD is largely unknown, and the basis for its progression to dementia (sPDD) is poorly characterized. We have identified that loss of IFNβ or IFNAR1, the receptor for IFNα/β, causes pathological and behavioral changes resembling PDD, prompting us to hypothesize that dysregulated genes in IFNβ-IFNAR signaling pathway predispose one to sPD. By transcriptomic analysis, we found defective neuronal IFNβ-IFNAR signaling, including particularly elevated PIAS2 associated with sPDD. With meta-analysis of GWASs, we identified sequence variants in IFNβ-IFNAR-related genes in sPD patients. Furthermore, sPDD patients expressed higher levels of PIAS2 mRNA and protein in neurons. To determine its function in brain, we overexpressed PIAS2 under a neuronal promoter, alone or with human α-synuclein, in the brains of mice, which caused motor and cognitive impairments and correlated with intraneuronal phosphorylated (p)α-synuclein accumulation and dopaminergic neuron loss. Ectopic expression of neuronal PIAS2 blocked mitophagy, increased the accumulation of senescent mitochondrial and oxidative stress, as evidenced by excessive oxDJ1 and 8OHdG, by inactivating ERK1/2-P53 signaling. Conversely, PIAS2 knockdown rescued the clinicopathological manifestations of PDD in Ifnb–/– mice on restoring mitochondrial homeostasis, oxidative stress, and pERK1/2-pP53 signaling. The regulation of JAK-STAT2-PIAS2 signaling was crucial for neurite outgrowth and neuronal survival and excitability and thus might prevent cognitive impairments. Our findings provide insights into the progression of sPD and dementia and have implications for new therapeutic approaches.

Next-generation wireless networks have to be robust and self-sustained. Internet of things (IoT) is reshaping the technological adaptation in the daily life of human beings. IoT applications are highly diverse, and they range from critical applications like smart city, health-based industries, to industrial IoT. Machine learning (ML) techniques are integrated into IoT to make the network efficient and autonomous. Deep learning (DL) is one of the types of ML, and it is computationally complex and expensive. One of the challenges is to merge deep learning methods with IoT to overall improve the efficiency of the IoT applications. An amalgamation of these techniques, maintaining a balance between computational cost and efficiency is crucial for next-generation IoT networks. In consideration of the requirements of ML and IoT and seamless integration demands overhauling the whole communication stack from physical layer to application layer. Hence, the applications build on top of modified stack will be significantly benefited, and It also makes it easy to widely deploy the network.

The analysis of fork-join queueing systems has played an important role for the performance evaluation of distributed systems where parallel computations associated with the same job are carried out and a job is considered served only when all the parallel tasks it consists of are served and then joined. The fork-join nodes that we consider consist of K ≥ 2 parallel servers each of which is equipped with two First Come First Served queues, namely the service-queue and the join-queue. The former stores the tasks waiting to be served while the latter stores the served tasks waiting to be joined. Under heavy load conditions, the variance of the service times associated with the tasks tends to cause long join-queue lengths. In this work, we propose an algorithm to dynamically control the servers’ speeds (e.g., via frequency scaling), that aims at reducing the power consumption of the servers whose join-queue lengths are longer than the others’. Under Markovian assumptions, we provide a model for the performance evaluation of the system in saturation that allows us to derive the expression for the steady-state distribution, the system’s throughput and balance index. Finally, we derive the analytical expression for the marginal state probabilities of each server and provide upper and lower bounds for the expected power consumption.

The introduction of the class of queueing networks called G-networks by Gelenbe has been a breakthrough in the field of stochastic modeling since it has largely expanded the class of models which are analytically or numerically tractable. From a theoretical point of view, the introduction of the G-networks has lead to very important considerations: first, a product-form queueing network may have non-linear traffic equations; secondly, we can have a product-form equilibrium distribution even if the customer routing is defined in such a way that more than two queues can change their states at the same time epoch. In this work, we review some of the classes of product-forms introduced for the analysis of the G-networks with special attention to these two aspects. We propose a methodology that, coherently with the product-form result, allows for a modular analysis of the G-queues to derive the equilibrium distribution of the network.

In the last decade, underwater wireless sensor networks (UWSNs) have attracted a lot of attention from the research community thanks to their wide range of applications that include seabed mining, military and environmental monitoring. With respect to terrestrial networks, UWSNs pose new research challenges such as the three-dimensional node deployment and the use of acoustic signals. Despite the large number of routing protocols that have been developed for UWSNs, there are very few analytical results that study their optimal configurations given the system's parameters (density of the nodes, frequency of transmission, etc.). In this paper, we make one of the first steps to cover this gap. We study an abstraction of an opportunistic routing protocol and derive its optimal working conditions based on the network characteristics. Specifically, we prove that using a depth threshold, i.e., the minimum length of one transmission hop to the surface, is crucial for the optimality of opportunistic protocols and we give a numerical method to compute it. Moreover, we show that there is a critical depth threshold above which no packet can be transmitted successfully to the surface sinks in large networks, which further highlights the importance of properly configuring the routing protocol. We discuss the implications of our results and validate them by means of stochastic simulations on NS3.

In the last decade, Underwater Wireless Sensor Networks (UWSNs) have been widely studied because of their peculiar aspects that distinguish them from common wireless terrestrial networks. In fact, most UWSNs use acoustic instead of radio-frequency based communications, and nodes are subject to high mobility caused by water currents. As a consequence, specialised routing algorithms have been developed to tackle this challenging scenario. Depth based Routing (DBR) is one of the first protocols that have been developed to this aim, and is still widely adopted in actual implementations of UWSNs. In this paper we propose a stochastic analysis that aims at evaluating the performance of UWSNs using DBR in terms of expected energy consumption and expected end-to-end delay. Under a set of assumptions, we give expressions for these performance indices that can be evaluated efficiently, and hence they can be adopted as the basis for optimizing the configuration parameters of the protocol.

Dynamic load balancing plays an important role in the management of data centres where a fair workload distribution may be hard to achieve. Dynamic load balancing algorithms are usually clustered into two families: receiver-initiated and sender-initiated. In the former approach, an idle node transfers some of the jobs of a highly loaded node to its queue, whereas in the latter it is the overloaded node that decides to send somewhere else some of the jobs that have been assigned to itself. The literature has proved the receiver-initiated strategies to be more effective than the sender-initiated ones under heavy load. In this paper, we give for the first time a closed form expression of the stationary distribution of a class of queueing networks where an arbitrary number of nodes implement a receiver-initiated dynamic load balancing algorithm. The result can be applied to Markovian queueing networks with probabilistic routing and exponentially distributed service times.

In this paper we address the problem of efficiently deriving the steady-state distribution for a continuous time Markov chain (CTMC) S evolving in a random environment E. The process underlying E is also a CTMC. S is called Markov modulated process. Markov modulated processes have been widely studied in literature since they are applicable when an environment influences the behaviour of a system. For instance, this is the case of a wireless link, whose quality may depend on the state of some random factors such as the intensity of the noise in the environment. In this paper we study the class of Markov modulated processes which exhibits separable, product-form stationary distribution. We show that several models that have been proposed in literature can be studied applying the Extended Reversed Compound Agent Theorem (ERCAT), and also new product-forms are derived. We also address the problem of the necessity of ERCAT for product-forms and show a meaningful example of product-form not derivable via ERCAT.

We model networks of Energy Packets which have been previously introduced by Gelenbe and his colleagues to represent the interactions between communication units and energy units in data processing networks with energy harvesting. We consider failures of batteries and the network structure models the connectivity. The model explicitly represents the amount of Energy Packets needed to transfer a Data Packet. We consider both Data Packets and Jumbo Data Packets which require distinct amounts of energy to be transmitted. Unlike previous models, our approach is based on the assumption that the transmission time of a Data Packet can be neglected when we model Energy Packets harvesting and Leakage which are operating on a larger time scale. We prove that the network of queues associated with the batteries has a product form steady-state distribution under usual Markovian assumptions. An important feature of our model is the ability to study Data Packet losses due to the lack of energy at certain nodes or to the failure of the components which cannot be obtained in previous models with closed form solutions.

Active Queue Management (AQM) techniques have been proposed to support scenarios with many connections sharing the same bottleneck. The basic idea is that a smart management of the bottleneck queue can avoid the saturation of the link and ensure a smoother use of the available bandwidth. This is generally achieved by exploiting the flux control mechanism of TCP and its behavior in case of packet losses or other explicit notifications. In this paper we consider classic and innovative AQM techniques and analyse their performance under different scenarios through the use of mean field models.

Internetworking often requires a large amount of users to share a common gateway to obtain connectivity to the Internet. Congestion avoidance mechanisms are used to prevent the saturation of the gateway which represents a bottleneck of the system. The most popular congestion avoidance mechanisms are the Explicit Congestion Notification (ECN) and the Random Early Detection (RED). Recently, a new method for the congestion avoidance has been proposed: the Smart Access Point with Limited Advertised Window (SAP-LAW). The main idea is to hijack the acknowledge packets in the TCP connections in order to artificially reduce the advertised destination window according to some bandwidth allocation policy. Therefore, the flux control mechanism is artificially exploited to control the congestion at the bottleneck. The advantage of this approach is that it does not require any modification in the TCP implementations at the clients. In this paper, we propose stochastic models for the ECN/RED and SAP-LAW mechanisms in order to compare their performances under different scenarios. The models are studied in mean field regime, i.e., under a great number of TCP connections and UDP based transmissions. Augmenting previous work on ECN/RED, we consider the presence of UDP traffic with bursts, and short lived TCP connections. The models for SAP-LAW are totally new. The comparison is performed in terms of different performance indices including average queue length, system throughput and expected queuing time.

Product-form models facilitate the efficient analysis of large stochastic models and have been sought after for some three decades. Apart from the dominating work on queueing networks, some product-forms were found for stochastic Petri nets (SPNs) that allow fork-join constructs and for queueing networks extended to include special customers called signals, viz. G-networks. We appeal to the Reversed Compound Agent Theorem (RCAT) to prove new product-form solutions for SPNs in which there are special transitions, the firings of which act in a similar way to signals in G-networks, but which may be generated by synchronised firings (or service completions) and may affect several places simultaneously. We show that SPNs with signals are strict generalisations of G-networks with negative customers, triggers and catastrophes, and illustrate with copious examples.

In this paper we propose a new model for the analysis of systems with aging objects such as Time-To-Live cache. We consider a model with an underlying Continuous Time Markov Chain in which objects can be completely or partially rejuvenated. In the former case the object becomes fresh, while in the latter all the objects are simultaneously rejuvenated so that the youngest becomes fresh. We show that under the so-called Independent Reference Model assumption our model is numerically tractable and has a product-form equilibrium distribution. Furthermore, we consider the case in which the object aging stops after a certain threshold and hence the partial rejuvenation introduces a probabilistic behaviour. Also in this case, we can derive a product-form equilibrium distribution under some mild conditions. The models presented in this paper may be interpreted as a new class of G-networks with catastrophes and partial flushing.

The complexity of modern cloud facilities requires attentive management policies that should encompass all aspects of the system. Security is a critical issue, as intrusions, misuse or denial of service attacks may damage both the users and the cloud provider including its reputation on the market. Disruptive attacks happen fast, cause evident and short term damages and are usually the result of operations that are hard to disguise. On the other hand, Energy oriented Denial of Service (eDoS) attacks aim at producing continuous minor damages, eventually with long term consequences. These long lasting attacks are difficult to detect. In this paper we model and analyse the behaviour of a system under eDoS attack. We study the impact in terms of cloud energy consumption of an attack strategy previously proposed in the literature and compare it with other strategies that we propose. Our findings show that the strategy previously proposed in the literature, based on keeping the cloud close to saturation, is not optimal (from the point of view of the attacker) in presence of non-constant workload and that there is a trade-off between the aggressiveness of the attacker and the duration of the attack in order to maximise the damage.

In Wireless Sensor Networks (WSN) a set of motes monitors the environment by measuring some physical phenomena such as humidity, light, temperature, vibrations. The coexistence of different data types arises the problem of assigning the network resources in a fair way by taking into account possible different priorities among the traffic streams. In this paper we propose an allocation control scheme which is easy to implement, meets the limited resources of sensor nodes, and does not require extra control traffic in the WSN. Our scheme is based on the idea that the motes maintain a window with the classes of the latest transmissions. We propose an analytical model and provide an algorithm to study the performance of this allocation scheme with respect to the throughput and the fairness achieved. The model is a continuous time Markov chain which is proved to be dynamically reversible (although not reversible) and admits a product-form equilibrium distribution.

This paper studies interconnected wireless sensors with the paradigm of Energy Packet Networks (EPN) which were previously introduced. In the EPN model, both data transmissions and the flow of energy are discretized, so that an energy packet (EP) is the minimum amount of energy (say in microjules) that is needed to process and transmit a data packet (DP) or to process a job. Previous work has modeled such systems to determine the relation between energy flow and DP transmission, or to study the balance between energy and the processing of jobs in Cloud Servers. The lack of energy, in addition to processing times, is the main source of latency in networks of sensor nodes. Thus this paper models this phenomenon, and shows that under some reasonable conditions, assuming feedforward flow of data packets and local consumption and leakage of energy, such networks have product form solutions.

A large class of modern servers are capable of providing services with different levels of quality. In general, more accurate output requires longer computation time, allowing the trade-off between quality of service and expected response time. In order to model a self-adaptive system that employs dynamic control of the quality of service, we study a queueing system with C classes of service, each of which is characterised by a quality of service and an expected service time. The routing of customers to classes occurs at the customer arrival epoch and depends on the number of customers in the system -- specifically, on C -- 1 thresholds that are parameters of the system. We aim at finding the values for these thresholds that maximise a reward function which we define based on the quality of provided service and expected response time. Differently from previous work, we consider processor sharing queueing discipline. We find the exact solution of the underlying Markov chain based model to be computationally too expensive for the purpose of maximising the reward function in a self-adaptive manner, and propose an approximate model which we use to maximise the reward function using deepest descent search.

Metastatic colorectal cancer (mCRC) is still one of the tumor types with the highest incidence and mortality. In 2012, colorectal cancer was the second most prevalence cancer among males (9%) and the third among females (8%). In this disease, early diagnosis is important to improve treatment outcomes. However, at the time of diagnosis, about one quarter of patients already have metastases, and overall survival of these patients at 5-years survival is very low. Because of these poor statistics, the development of new drugs against specific targets, including the pathway of angiogenesis, has witnessed a remarkable increase. So, targets therapies through epidermal growth factor and its receptor and also KRAS pathways modulation acquired a main role whether in association with standard chemotherapy and radiotherapy. With the current knowledge in the field of molecular biology, including genetic mutations and polymorphisms, we know better why patients respond so differently to the same treatments. So, in the future we can develop increasingly personalized treatments to the patient and not the disease. This review aims to summarize some molecular pathways and their relation to tumor growth, as well as novel targeted developing drugs and recently approved for mCRC. (C) 2014 Baishideng Publishing Group Inc. All rights reserved. info:eu-repo/semantics/publishedVersion

Multiformalism modeling has shown to be a valuable technique to cope with the complexity of the constraints that apply to specifications of computer-based systems state of the art. Multiformalism techniques help modelers and designers by providing a more (natural and) convenient approach in the specification process and in analysis of performance. Although their application does not necessarily provide an advantage in the solutions of the models, this paper shows how a compositional multiformalism modeling approach can leverage the power of product-form solutions to offer both efficient solution and specification of models for complex systems.

We present a framework to automatically evaluate the performance of Mobile Ad-hoc Networks (MANETs) in terms of different kinds of metrics, such as throughput and energy consumption. We use a probabilistic process calculus to model MANETs; we translate process terms into Markov Decision Processes (MDPs) and use the probabilistic model checker PRISM to automatically evaluate the network performance. We present a case study consisting of a network which uses flooding for communicating, and we analyse how time and energy costs vary when pursuing different power control strategies.

Mobile ad-hoc and sensor networks play an important role in several application fields. The usage of wireless links and the node mobility make the networks prone to security attacks; among these, jamming attacks are insidious and they consist of one or more nodes continuously transmitting dummy packets to keep some wireless links busy. The goal is to destroy the network connectivity or highly reduce its throughput. In this paper we propose a probabilistic formal method, based on a process algebraic approach, targeted at the analysis of connectivity and the evaluation of interference in mobile networks. We show our framework at work on the analysis of an indoor wireless communication scenario.

Many wireless protocols (including 802.11 definitions) consider fragmentation a valuable approach to minimise the number of transmissions which are lost due to errors. Deciding the optimum size of the fragments is a problem that has been deeply studied in literature. In this paper we consider a different perspective: we propose a Markovian queueing model capable of predicting important performance measures — e.g., the expected response time, the expected number of enqueued packets — of protocols that adopt fragmentation as a mechanism for improving the maximum system throughput. Modelling the fragmentation is a non-trivial problem because batches of fragments have to be handled and the coefficient of variation of the time required to send a fragment is usually very low since almost all the frames tend to have the same size. We use the model to derive some performance indices under two scenarios: a multimedia transmission and a secure, encrypted, communication.

International audience; In this paper, we propose a general framework to evaluate and compare both throughput and energy efficiency of mobile ad hoc networks. This framework is built upon a probabilistic, energy-aware, calculus for the analysis of broadcast, unicast and multicast wireless communications. The results of our analyses can be used to replace a network component with a more efficient one - with respect to different metrics like throughput and energy consumption - while maintaining connectivity. As an application, we evaluate two well-known automatic repeat request (ARQ)-based error control protocols: stop-and-wait (SW) and go-back-N (GBN).

In queueing networks with blocking, stations wishing to transmit customers to a full queue are blocked and need to take alternative action on completing a service. In general, product-forms, i.e. separable solutions for such a network's equilibrium state probabilities, do not exist but some product-forms have been obtained over the years in special cases, using a variety of techniques. We show that the Reversed Compound Agent Theorem (RCAT) can obtain these diverse results in a uniform way by its direct application, so unifying product-forms in networks with and without blocking. New product-forms are also constructed for a type of blocking we call `skipping', where a blocked station sends its output-customers to the queue after the one causing the blocking in that customer's path. Finally, we investigate a novel congestion management scheme for networks of finite-capacity queues in which a station with a full queue transmits signals that delete customers from upstream queues in order to reduce incoming traffic.

In this paper we provide a general method to derive product-form solutions for stochastic models. We take inspiration from the Reversed Compound Agent Theorem and we provide a different formulation using labeled automata, a generalization which encompasses a bigger class of product-form solutions, and a new proof based on the solution of the system of global balance equations. We show that our result may have practical applications in the performance evaluation of complex software and hardware architectures and can be the base for the development of new analysis tools or the extension of existing ones.

Performance evaluation of systems including, among others, components based on wireless links plays an important role both in the software and hardware engineering. In both cases the earlier evaluation of performance characteristics has been proved to be a key-factor for a successful design of new systems. However, most of the quantitative analysis methods suffers the problem of not being scalable with respect to the number of system components, due to the state space explosion problem. In this paper we address the problem of the quantitative modelling of system with some unreliable wireless links (i.e., where packet loss and retransmissions must be considered) using a set of results of product-form theory. Product-form models present separable equilibrium distributions of the state probabilities thus making the derivation of the desired performance indices (e.g., mean number of customers or mean response time) computationally tractable. This work exploits recent advances in this field, e.g., the Reversed Compound Agent Theorem, to efficiently evaluate the performance of systems with unreliable wireless links.

Queueing networks with multiple classes of customers play a fundamental role for evaluating the performance of both software and hardware architectures. The main strength of product–form models, in particular of BCMP queueing networks, is that they combine a flexible formalism with efficient analysis techniques and solution algorithms. In this paper we provide an algorithm that starting from a high–level description of a system, and from the definition of its components in terms of interacting sub–systems, computes a multiple–class and multiple–chain BCMP queueing network. We believe that the strength of this approach is twofold. First, the modeller deals with simplified models, which are defined in a modular and hierarchical way. Hence, we can carry on sensitivity analysis that may easily include structural changes (and not only on the time parameters). Second, maintaining the product–form property allows one to derive the average system performance indices very efficiently. The paper also discusses the application of the algorithm for the performance evaluation of websites with modular architectures, such as those based on content management systems.

Colorectal cancer (CRC) has high incidence and mortality worldwide. In 2012, CRC was the second most prevalent cancer among males (9%) and the third among females (8%). In recent decades, standard chemotherapies protocols combining 5-fluorouracil, leucovorin, irinotecan and oxaliplatin were important for improve survival in this set of patients. Further, biological drugs throughout epidermal growth factor receptor (EGFR) pathways showed interesting results in metastatic disease (mCRC) control when in association to standard chemotherapy regimens. Cetuximab and panitumumab are two cornerstones for mCRC treatment and are both approved in Europe and United States based on previous results phase III trials. This paper will briefly summarize those anti-EGFR therapies framework in mCRC and discusses some issues in this regard.

Gastric cancer usually is diagnosed in advanced stages and thus current medical practice affords limited therapeutic options. However, recent studies established the role of human epidermal growth factor receptor 2 (HER2) in clinical management. Trastuzumab, an anti-HER2 monoclonal antibody, acquired a main role in advanced gastric cancer harboring HER2 overexpression and/or amplification improving survival to 17.1 mo according to trastuzumab for gastric cancer phase III trial results. Also, new promising drugs, such as c-Met inhibitors, are in development and assessment for this setting. Certainly, novel drugs will emerge in the next feel years for help oncologists improve clinical management of advanced gastric cancer providing higher survival and quality of life. In this mini-review we will discuss some issues in this regard and provide an actual overview of this setting.

In this paper we present the recently introduced Three-Valued Spatio-Temporal Logic (TSTL), which extends the available spatio-temporal analysis of stochastic systems, and an automatic procedure to verify whether this analysis satisfies given reliability requirements. The novel spatio-temporal logic TSTL enriches the analysis of properties expressed in Signal Spatio-Temporal Logic (SSTL), providing further insight into the dynamic behaviour of systems. Starting from the estimated satisfaction probabilities of given SSTL properties, it enables the analysis of their temporal and spatial evolution. We use a three-valued approach in our verification procedure to include the uncertainty associated with the simulation-based statistical method used to estimate the satisfaction probabilities. In relation to this aspect, we introduce a reliability specification for the TSTL analysis and we present a specific algorithm to automatically assess whether it is satisfied by the evaluation of TSTL formulas. © 2017 ACM.

Exceptions constitute a widely accepted fault tolerance mechanism, suitable to manage both hardware and software faults. In performability analysis it is a common practice to exploit software tools capable of describing a system using models expressed in various formalisms. Often these tools provide extensibility features that allow augmenting the primitives of a given formalism, but in most cases they lack of exception support. This paper aims at filling this gap, by introducing a general mechanism to add support for exception handling to most of the existing formalisms. The validity of the proposed method is supported by two modelling cases that benefit in clarity and economy.