Memory embedded in Markov models specified in Statecharts: simulation versus analytical approaches

Reactive systems respond to internal or external stimuli and act in an event-driven manner. It is generally difficult to specify a complex reactive systems behavior. One reason is that actual reactive systems are usually formed by combining plural sequential machines that behave concurrently. This paper presents the state diagram matrix (SDM) which is a visual and hierarchical formalism of a reactive system's behavior. SDM involves two concepts. The first is a matrix plane description, on which a 3-dimensional state space is projected. The second is state composition and state abstraction for hierarchical sequential machine definition. The Petri net model is used for the composition of parallel sequential machines. The understandability and reliability of control software is shown to be improved as a consequence of adopting SDM for specifying disk-subsystem control requirements. The development support functions of SDM using a workstation are also described. >

The sleep state is traditionally defined as global, involving the entire body. Accordingly, a two-process mathematical model of the switch between sleep and wakefulness was proposed by Borbely more than 30 years ago and successfully used in deep modeling studies. However, in recent decades, new data have been obtained that call for a more complex understanding of sleep, both in spatial and temporal aspects. In particular, this concerns such phenomena as local sleep and short sleep episodes (naps). Here, we propose a mathematical model that gives the space for both local sleep phenomena and short sleep episodes. In doing so, we rely on the concept of a "sleep unit" and propose a phenomenological rate model of its activity. We also use a concept of a psycho-sensory drive to simulate various scenarios induced by external or internal stimuli. An essential element of our model is the representation of the background neural activity as a random signal, which reflects the real situation of overlapping multiple internal and external stimuli. The corresponding simulation runs confirm that the model reproduces both phenomena: local sleep and short nap episodes. As a particular but illustrative result, we demonstrate an example of involuntary global falling asleep under conditions of an overload of a separate sleep unit. In conclusion, we discuss the potential of our model and ways of its further quantitative parameterization.

The field of human factors studies the interaction between humans and technological systems in order to optimize human–system performance, research that has traditionally been focused on observation and experiment in laboratory and real-world settings. However, a multidisciplinary subfield of human factors called human performance modeling has recently emerged that involves the development and application of mathematical models, computer simulation techniques, and computational data analysis methods to the study of human performance. This chapter provides an introduction to the use of computational modeling, simulation, and analysis techniques in human factors research, with an eye toward how such methods may be used to optimize human performance in extreme settings.

Stimuli-responsive nanoliposomes as prospective nanocarriers for targeted drug delivery

The role of cognitive and affective responses in the relationship between internal and external stimuli on online impulse buying behavior

PurposeThe key challenge that urban cities in most developing and transitional economies is confronting is municipal solid waste (MSW) management. Waste separation is a critical component to successful recycling management in terms of enhancing the quality of recyclables, reducing MSW and optimizing incineration. The urge to actualizing sustained waste separation behavior has been hindered by potential barriers. This study aims to examine the influences of external and internal stimuli of targeted households' waste separation intention in parts of China.Design/methodology/approachA multifactor framework predicting the process that leads to waste separation attitude and behavioral intention is proposed. SEM analysis is conducted in SmartPLS based on 371 survey questionnaires collected in Nanning city in China.FindingsPolicy regulation is the biggest determinant of attitude among external stimuli, while awareness of consequence has the strongest relationship with an attitude among internal stimuli. Facilitating conditions, subjective norms and moral norms are all significant predictors of attitude. As a result, increasing positive attitude leads to enhance waste separation lifestyle.Research limitations/implicationsThis study adopts a cross-sectional design to investigate the waste separation intention of local households. Data collection is restricted to one point in time for every individual. A mixed method is recommended. Quantitative research can examine variables provided in existing literature with numerical analysis. Qualitative research might be helpful to identify other unknown factors. Also, the survey questionnaires employ a self-reported manner, and respondents might be overrating to avoid embarrassment.Practical implicationsFuture research is recommended to engage observation at houses or at the waste-collecting points for actual waste separation behavior. Moreover, this study measures intention toward household waste separation, but whether this intention will eventually lead to waste separation behavior is not a guarantee. Future study is recommended to examine whether intention translates into actual waste separation behavior.Originality/valueEmphasizing the importance of policy element as a direct influence toward attitude, this paper focuses on the waste separation attitude accumulated from external and internal stimuli, and, concurrently, waste separation behavioral intention is influenced by accumulated attitudes. The study provides relevant policy development information of three Asian countries to enhance their present and future policy directions for a sustainable household waste separation management process

This paper studies performance modeling of the IEEE 802.11p enhanced distributed channel access (EDCA) mechanism and develops performance models to analyze the access performance of the IEEE 802.11p EDCA mechanism. A 2-D Markov chain is first constructed to model the backoff procedure of an access category (AC) queue and establish a relationship between the transmission probability and collision probability of the AC queue. Then, a 1-D discrete-time Markov chain is constructed to model the contention period of an AC queue and establish another relationship between the transmission probability and collision probability of the AC queue. Unlike most existing work, the 1-D Markov chain is extended to be infinite in modeling the contention period of an AC queue under both saturated and nonsaturated conditions. The two Markov models take into account all major factors that could affect the access performance of the IEEE 802.11p EDCA mechanism, including the saturation condition, standard parameters, backoff counter freezing, and internal collision. Based on the two Markov models, performance models are further derived to describe the relationships between the parameters of an AC queue and the access performance of the AC queue in terms of the transmission probability, collision probability, normalized throughput, and average access delay, respectively. The effectiveness of the performance models are verified through simulation results.

Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of ‘general Markov models’, providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results.

Stimuli-responsive peptide-based biomaterials as drug delivery systems

A methodology for transferring principles of plant movements to elastic systems in architecture

Sequential reactive systems include hardware devices and software programs which operate in continuous interaction with the external environment, from which they receive streams of input signals (data, commands) and in response to them form streams of output signals. Systems of this type include controllers, network switches, program interpreters, system drivers. The behavior of some reactive systems is determined not only by the sequence of values of input signals, but also by the time of their arrival at the inputs of the system and the delays in computing the output signals. These aspects of reactive system computations are taken into account by real-time models of computation which include, in particular, realtime finite state machines (TFSMs). However, in most works where this class of real-time automata is studied a simple variant of TFSM semantics is used: the transduction relation computed by a TFSM is defined so that the elements of an output stream, regardless oftheir timestamps, follow in the same order as the corresponding elements ofthe input stream. This straightforward approach makes the model easier to analyze and manipulate, but it misses many important features of real-time computation. In this paper we study a more realistic semantics of TFSMs and show how to represent it by means of Labeled Transition Systems. The use of the new TFSM model also requires new approaches to the solution of verification problems in the framework of this model. For this purpose, we propose an alternative definition of TFSM computations by means of Labeled Transition Systems and show that the two definitions of semantics for the considered class of real-time finite state machines are in good agreement with each other. The use of TFSM semantics based on Labeled Transition Systems opens up the possibility of adapting well known real-time model checking techniques to the verification ofsequential reactive systems.

This paper proposes a new hierarchical circuit modeling and simulation technique in s-domain for linear analog circuits. The new algorithm can perform circuit complexity reduction by deriving the exact or approximate admittances in rational form in the reduced circuit matrix and deriving the circuit characteristics for very large linear analog and interconnect circuits. We characterize some theoretical results regarding the conditions on the generations of canceling terms during the general hierarchical circuit analysis and propose an explicit de-cancellation scheme to remove canceling terms based on a new hierarchical symbolic analysis framework. The resulting algorithm can be used for modeling and simulation of linear analog and interconnect circuits in both frequency and time domain.

An approach to user modelling with discrete stochastic processes is presented, which aims at the dynamic individualization of user interfaces on the syntactic layer. The state of the art of syntactic user modelling is surveyed. The mathematical background of simple Markov Chains and the 'classic' Hidden Markov Model is presented. Furthermore, dynamic Bayesian Networks are introduced, which generalize these Markovian Models. A case study of the simulation experiments uses a multimodal user interface for supervisory control of advanced manufacturing cells. A corresponding simulation model is created and exploited to generate interaction cases, which are the empirical basis for the evaluation. Six topologies of dynamic Bayesian Networks are evaluated for 100 interaction cases and 50 replications each: (1) Markov Chain of order 1, (2) Hidden Markov Model, (3) autoregressive Hidden Markov Model, (4) factorial Hidden Markov Model, (5) simple hierarchical Hidden Markov Model, and (6) tree structured Hidden Markov Model. The dependent variable is the prediction accuracy for a single prediction lead. In a first step, a one-way analysis of variance in conjunction with Tukey's post-hoc test demonstrated a significant superiority of the simple hierarchical Hidden Markov Model. In a second step, an additional two-way analysis of variance also indicated a significantly better prediction accuracy of the simple hierarchical Hidden Markov Model compared to the Hidden Markov Model, but the number of interaction cases also had a significant effect. Hence, the modeller has to take both factors--model topology and number of interaction cases--into account when designing syntactic user models with stochastic processes.

Heat integration between vapor and liquid streams has been widely used in chemical and petrochemical plants for conventional distillation processes as an alternative to reduce the energy consumption. However, with the advances that have been proposed in intensified distillation processes in the last couple of decades, heat-integrated alternatives that are more attractive than the typical condenser–reboiler heat integration have also been proposed. Therefore, intensified distillation processes also need a new approach methodology to implement optimal locations and heat load in heat-integrated distillation. This chapter aims to cover the fundamentals, simulation and optimization approaches for heat-integrated intensified distillation processes for nonreactive and reactive systems. Conventional distillation can result in an intensified process if heat integration is allowed at locations other than the condenser and reboiler. Although thermally coupled distillation and Heat-Integrated Distillation (HIDiC) are already intensified processes, they can attain higher energy reduction by rearranging their heat load distribution. For reactive systems, at the location subject to heat integration, vapor–liquid equilibrium and reaction kinetic conditions are modified simultaneously, which results in a very challenging problem. Reactive system via multi-effect and thermally coupled configuration is also covered in this chapter for the methyl acetate hydrolysis and esterification of isopropyl alcohol. The applications of heat-integrated intensified distillation show feasible solutions with improved energy efficiency and total annual cost reduction for new designs.

A hierarchy of models that capture realistic aspects of reactive and real-time systems is introduced. On the most abstract level, the qualitative (non-quantitative) model captures the temporal precedence aspect of time. A more refined model is that of timed transition systems, representing the metric aspects of time. The third and most detailed model is that of hybrid systems. This model allows the incorporation of continuous components into the studied reactive system.