Occurrence Of Specific Events Research Articles

Simulations for Event-Clock Automata

Event-clock automata (ECA) are a well-known semantic subclass of timed automata (TA) which enjoy admirable theoretical properties, e.g., determinizability, and are practically useful to capture timed specifications. However, unlike for timed automata, there exist no implementations for checking non-emptiness of event-clock automata. As ECAs contain special prophecy clocks that guess and maintain the time to the next occurrence of specific events, they cannot be seen as a syntactic subclass of TA. Therefore, implementations for TA cannot be directly used for ECAs, and moreover the translation of an ECA to a semantically equivalent TA is expensive. Another reason for the lack of ECA implementations is the difficulty in adapting zone-based algorithms, critical in the timed automata setting, to the event-clock automata setting. This difficulty was studied by Geeraerts et al. in 2011, where the authors proposed a zone enumeration procedure that uses zone extrapolations for finiteness. In this article, we propose a different zone-based algorithm to solve the reachability problem for event-clock automata, using simulations for finiteness. A surprising consequence of our result is that for event-predicting automata, the subclass of event-clock automata that only use prophecy clocks, we obtain finiteness even without any simulations. For general event-clock automata, our new algorithm exploits the G-simulation framework, which is the coarsest known simulation relation in timed automata literature, and has been recently used for advances in other extensions of timed automata.

Analysis of the Impact of Fund Behavior on WTI Crude Futures Market

In recent years, uncertainties in the international community have increased, and the factors affecting crude oil futures prices are often diversified. This paper studies the driving factors of three dimensions that influence the price of crude oil and focuses on the influence of the financial dimension on the price of crude oil. At the same time, the multivariate regression model is constructed to conduct an empirical analysis to judge the factors that can affect the crude oil futures price and the correlation between the occurrence of special events and the crude oil price fluctuation.

Ransomware prevention using moving target defense based approach

AbstractOver the past decade, there has been a rapidly rising trend of malware (ransomware) that limits user access by encrypting the data and demanding the ransom against the decryption key. In most cases, such encryption may lead to a permanent data loss. In order to prevent this unwanted encryption, we propose a method based on Moving Target Defense (MTD) approach. Our method is based on the alteration of the attack surface to reduce the attack success ratio. We have used multiple layers of MTD. The first layer generates random extensions that hide the existing known file extensions. This will protect user files against those ransomware variants which encrypt files having some specific extensions. Our second layer of protection uses event‐based MTD in which tasks are scheduled to change file extensions at the occurrence of specific events which mostly occur due to the execution of ransomware in the system. As a result of our proposed method, we have successfully protected user files against well‐known ransomware variants such as WannaCry, Cerber, Locky, Tesla, Revil, Bitlocker, Darkside, Ranzy.

Data-Enhancement Strategies in Weather-Related Health Studies.

Although the relationship between weather and health is widely studied, there are still gaps in this knowledge. The present paper proposes data transformation as a way to address these gaps and discusses four different strategies designed to study particular aspects of a weather–health relationship, including (i) temporally aggregating the series, (ii) decomposing the different time scales of the data by empirical model decomposition, (iii) disaggregating the exposure series by considering the whole daily temperature curve as a single function, and (iv) considering the whole year of data as a single, continuous function. These four strategies allow studying non-conventional aspects of the mortality-temperature relationship by retrieving non-dominant time scale from data and allow to study the impact of the time of occurrence of particular event. A real-world case study of temperature-related cardiovascular mortality in the city of Montreal, Canada illustrates that these strategies can shed new lights on the relationship and outlines their strengths and weaknesses. A cross-validation comparison shows that the flexibility of functional regression used in strategies (iii) and (iv) allows a good fit of temperature-related mortality. These strategies can help understanding more accurately climate-related health.

Studying the capabilities of the analytical system based on the machine learning method

Data analysis carried out by machine learning tools has covered almost all areas of human activity. This is due to a large amount of data that needs to be processed in order, for example, to predict the occurrence of specific events (an emergency, a customer contacting the organization’s technical support, a natural disaster, etc.) or to formulate recommendations regarding interaction with a certain group of people (personalized offers for the customer, a person’s reaction to advertising, etc.). The paper deals with the possibilities of the Multitool analytical system, created based on the machine learning method «decision tree», in terms of building predictive models that are suitable for solving data analysis problems in practical use. For this purpose, a series of ten experiments was conducted, in which the results generated by the system were evaluated in terms of their reliability and robustness using five criteria: arithmetic mean, standard deviation, variance, probability, and F-measure. As a result, it was found that Multitool, despite its limited functionality, allows creating predictive models of sufficient quality and suitable for practical use.

INRISCO: INcident monitoRing in Smart COmmunities

Major advances in information and communication technologies (ICTs) make citizens to be considered as sensors in motion. Carrying their mobile devices, moving in their connected vehicles or actively participating in social networks, citizens provide a wealth of information that, after properly processing, can support numerous applications for the benefit of the community. In the context of smart communities, the INRISCO [1] proposal intends for (i) the early detection of abnormal situations in cities (i.e., incidents), (ii) the analysis of whether, according to their impact, those incidents are really adverse for the community; and (iii) the automatic actuation by dissemination of appropriate information to citizens and authorities. Thus, INRISCO will identify and report on incidents in traffic (jam, accident) or public infrastructure (e.g., works, street cut), the occurrence of specific events that affect other citizens' life (e.g., demonstrations, concerts), or environmental problems (e.g., pollution, bad weather). It is of particular interest to this proposal the identification of incidents with a social and economic impact, which affects the quality of life of citizens.

The subjects of the offenses to the norms on safety and health at work and the social and organizational effects specific to companies conducting mining activities

The offenses concerning safety and health at work have no material object, their constitutive content including: the object and subject of the offense, the objective and subjective side of the offense. The active subjects of an offense have a legal obligation to apply and control the implementation of occupational safety and health measures and the passive subjects will bear the consequences of the occurrence of specific events. The existence of some uncertainties in the current legislation, also affecting mining companies, creates legal disputes especially in relation to the issues concerning active subjects. As for the passive subjects, the legal loophole refers to the situation of the day-labourers who may be involved in accidents at work and occupational illnesses. The authors consider it is necessary to formulate new law articles for clarification and even to draw up a future law with concrete reference to day-labourers, but also to some new risk sensitive social groups. Thus, the egal work relationships: the manner of performing the work, its remuneration and subordination to the beneficiary carrying out mining activities, will also be specified.

Discrete-Event Systems Supervisory Control for a Custom Power Park

This paper investigates the application of supervisory control of discrete event system (SCDES) to the management and control of a custom power park (CPP). The heterogeneous nature of upcoming devices and equipment in CPP require advanced control methods to ensure the integrity and reliability under different operational states. A solution to achieve appropriate controllability, while avoiding complexity, is to sub-divide the control problem based on event-triggered dynamics where the occurrence of specific events could change the state of the system. This idea is employed to formulate the problem of coordination of the devices in a CPP and develop a systematic method to design a supervisory control based on the theory of SCDES. Three modular supervisors are synthesized using the TCT software and simulated using the Simulink. The proposed methodology could be applied to several control problems in microgrids.

Event-Based Estimation With Information-Based Triggering and Adaptive Update

This paper is motivated by recent advancements of cyber-physical systems and significance of managing limited communication resources in their applications. We propose an open-loop estimation strategy with an information-based triggering mechanism coupled with an adaptive event-based fusion framework. In the open-loop topology considered in this paper, a sensor transfers its measurements to a remote estimator only in occurrence of specific events (asynchronously). Each event is identified using a local stochastic triggering mechanism without incorporation of a feedback from the remote estimator and/or implementation of a local filter at the sensor level. We propose a particular stochastic triggering criterion based on the projection of local observation into the state-space, which in turn is a measure of the achievable gain in the local information state vector. Then, we investigate an unsupervised fusion model at the estimation side where the estimator blindly listens to its communication channel without having a priori information of the triggering mechanism of the sensor. An update mechanism with a Bayesian collapsing strategy is proposed to adaptively form state estimates at the estimator side in an unsupervised fashion. The estimator is adaptive in the sense that it is able to distinguish between having received an actual measurement or noise. The simulation results show that the proposed information-based triggering mechanism significantly outperforms its counterparts specifically in low communication rates, and confirms the effectiveness of the proposed unsupervised fusion methodology.

P2.02-003 Increased Circulating Cytokeratin-19 (Cyfra 21-1) is Predictive of Poor Outcome of Locally Advanced Squamous Cell Carcinoma in Lung

Our goal was to evaluate the prognostic significance of circulating tumor markers in locally advanced squamous cell carcinoma of lung (LA-SCCL). Eligible patients included those with histologically proven LA-SCCL, available baseline tumor marker panel analysis (carcino-embryonic antigen [CEA], carcinoma antigen 125 [CA125], squamous cell carcinoma antigen [SCC], cytokeratin-19 [Cyfra 21-1] and neuron-specific enolase [NSE]) and receiving definitive radiotherapy. Age, gender, radiation dose, baseline KPS, smoking history, weightless, TNM stage, PET staging, RT technique and treatment modality (radiotherapy alone vs. sequential chemoradiotherapy vs. concurrent chemoradiotherapy) were also retrospectively collected. To dichotomize the continuous values of tumor markers into categorical variables, ROC analysis was adopted to identify the optimal cutoff values using the progression within 2 years after diagnosis as the endpoint. Cox regression based multivariate analyses were used to select independent factors correlated with various survival endpoints. Overall survival (OS), local regional progression free survival (LRPFS) and distant metastasis free survival (DMFS) were defined as the time from diagnosis until the first occurrence of specific event: death, local-regional recurrence or distant metastasis, respectively. Progression free survival (PFS) was defined as the duration between the cancer diagnosis and the date of any progression or cancer related death. A total of 216 patients with LA-SCCL were analyzed. The optimal discriminative values for CEA, CA125, SCC, Cyfra 21-1 and NSE in predicting 2-y progression were 5.3 ng/ml, 17.0 U/ml, 2.5 ng/ml, 5.2 ng/ml and 17.8 ng/ml, respectively. Univariate analyses showed that increased Cyfra 21-1 was associated with inferior OS, LRPFS, DMFS and PFS. Increased NSE was predictive of poor OS, DMFS and PFS. CEA also presented significant correlation with OS. Under multivariate analysis involving all clinical and tumor markers, IIIA stage, better performance status, CEA ≤ 5.3 ng/ml and Cyfra 21-1 ≤ 5.2 ng/ml were independently associated with improved OS. IMRT technique, RT dose ≥ 60Gy and Cyfra 21-1 ≤ 5.2 ng/ml were correlated with better LRPFS. None-smoker, IIIA stage, NES ≤ 17.8 ng/ml were favorable predictors for DMFS. IIIA stage, KPS ≥ 80 and Cyfra 21-1 ≤ 5.2 ng/ml were advantageous factors related with favorable PFS. Baseline tumor marker panel including Cyfra 21-1, NSE and CEA can be prognostic of OS, local and distant tumor control for LA-SCCL, and should be recommended for baseline evaluation of tumor burden.

An Analysis of the Impact of Sudden Events on China's Automobile Industry Stock Price

The occurrence of special events such as food safety, information disclosure, policy implementation and political events will have a certain impact on the relevant areas of economic and financial. This paper uses the incident research method to take the political events of the Diaoyu Islands dispute as an example to study the influence of the listed companies in the Chinese automobile industry related to the Japanese brand. The conclusion is that the occurrence of special events in the listed companies of China's automobile industry to the relevant economic performance as a substitute effect, and the occurrence of special events on the Japanese brand of the car has a negative impact, but there is a positive impact on the Chinese local brands and European and American brands of cars.

Coordination in Networks of Linear Impulsive Agents

Consensus in heterogeneous networks containing both linear and linear impulsive dynamics is considered in this paper. The model applies for networks of interconnected dynamical systems, called agents, that are partitioned into several clusters. Most of the agents can only update their state in a continuous way using only inner-cluster agent states. On top of this, few agents also have the peculiarity to update their states in a discrete way by reseting it using states from agents outside their clusters. Our main result gives sufficient conditions for consensus in these networks. We firstly analyze the case when the reset sequence verifies some explicit time conditions. Secondly we consider the case when the reset instants are event-triggered, i.e., defined by the occurrence of specific events. Finally, we treat the case when the reset instants arrive stochastically following a Poisson renewal process.

Using Multiple Informants to Assess Child Maltreatment: Concordance between Case File and Youth Self-Report

To understand the psychosocial implications of child maltreatment, methods used to document prevalence must be clear. Yet, rates of maltreatment found in child self-report are generally inconsistent with data found in case files from state social service agencies. Although it is known that self-reports and case file reports of abuse disagree on occurrence of specific events, it is unclear if reporters agree when overall categories of abuse are considered. This study investigated differences between case file and youth report of abuse by examining four types of abuse—physical, sexual, neglect, and psychological—in a within-subjects design using a sample of 97 youth in foster care aged 8 to 22. Case files were coded for the presence of any indication of each type of abuse. Self-report of abuse was also assessed for any indication of each type of abuse. Results indicated that, overall, youth reported more physical and psychological abuse, and younger youth reported more sexual abuse than documented in their file. Implications for research and service provision for maltreated youth are discussed.

Lesser Degrees of Explanation: Some Implications of F.A. Hayekks Methodology of Sciences of Complex Phenomena

From the early-1950s on, F.A. Hayek was concerned with the development of a methodology of sciences that study systems of complex phenomena. Hayek argued that the knowledge that can be acquired about such systems is, in virtue of their complexity (and the comparatively narrow boundaries of human cognitive faculties), relatively limited. The paper aims to elucidate the implications of Hayek’s methodology with respect to the specific dimensions along which the scientist’s knowledge of some complex phenomena may be limited. Hayek’s fallibilism was an essential (if not always explicit) aspect of his arguments against the defenders of both socialism ([1935] 1948, [1940] 1948) and countercyclical monetary policy ([1975] 1978); yet, despite the fact that his conceptions of both complex phenomena and the methodology appropriate to their investigation imply that ignorance might beset the scientist in multiple respects, he never explicated all of these consequences. The specificity of a scientific prediction depends on the extent of the scientist’s knowledge concerning the phenomena under investigation. The paper offers an account of the considerations that determine the extent to which a theory’s implications prohibit the occurrence of particular events in the relevant domain. This theory of “predictive degree” both expresses and – as the phenomena of scientific prediction are themselves complex in Hayek’s sense – exemplifies the intuition that the specificity of a scientific prediction depends on the relevant knowledge available.

Using event-history analysis to examine the causes of semi-sedentism among shifting cultivators: a case study of the Haudenosaunee, AD 1500–1700

Event-history analysis is often used in the social sciences to study the occurrence of particular events over the lifespan of individuals and the impact of various factors on the rate at which those events occur. Like individuals, settlements can be analyzed and important events, such as founding or abandonment, can be studied using this method. Thus, as Richard Paine (1992) has noted, the technique can be useful in archaeological investigations of settlement processes. In this research we use it to explore the causes of settlement abandonment among a temperate shifting cultivators in an attempt to better understand the ecology of this adaptation and in order to evaluate the merits of the method. The sixteenth- and seventeenth-century Haudenosaunee (Iroquois) of northeastern North America present an interesting case study for applying event-history analysis to archaeological settlement dynamics because of the wealth of archaeological settlement data and detailed historic record that allows us to parse out historical factors and analyze the remaining ecological factors. We use event-history analysis as an alternative means for evaluating the relative and absolute effects of several variables that may have been predictive of the lifespan of settlements and the timing of their removal. The results suggest that the decision to move from a village was a complex process in which the population size of a village may have been the single most important, but not the only, determinant of settlement duration.

Spatio-Temporal Distribution of Extreme Weather Events in India

Alteration in characteristics of extreme weather events is projected in most part of the world, in changing climate due to increase in greenhouse gases and aerosols in the atmosphere. The changes in patterns in extreme weather events would lead to issues including energy, water and food securities. Therefore, it is important to study pattern in the events. In the study, we have selected ten key climate extreme events namely, flood, tropical cyclone, heat wave, cold wave also gale, squall, lightning, dust-storm, hailstorm and thunderstorm to study their recent past spatio-temporal pattern over India. Data on the occurrence have been acquired from India Meteorological Department and other relevant government agencies. Flood constitutes major share of the events. Cyclonic events with negligible share in occurrence have sizeable impact on socio-economic system. Regression analysis on the annual total number of occurrences of the combined events reveals a significant increasing trend. Except cyclone, all the events show increasing trend. Leading states by event category has also been computed and found that few states are relatively more prone to the repeated occurrence of particular events. Finally, we have concluded with the suggestions for the improvement in data collection and key recommendations for further study.

INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS

The probability density function (PDF), and its corresponding cumulative density function (CDF), provide direct statistical insight into the characterization of a random process or field. Typically displayed as a histogram, one can infer probabilities of the occurrence of particular events. When examining a field over some two-dimensional domain in which at each point a PDF of the function values is available, it is challenging to assess the global (stochastic) features present within the field. In this paper, we present a visualization system that allows the user to examine two-dimensional data sets in which PDF (or CDF) information is available at any position within the domain. The tool provides a contour display showing the normed difference between the PDFs and an ansatz PDF selected by the user and, furthermore, allows the user to interactively examine the PDF at any particular position. Canonical examples of the tool are provided to help guide the reader into the mapping of stochastic information to visual cues along with a description of the use of the tool for examining data generated from an uncertainty quantification exercise accomplished within the field of electrophysiology.

Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps

This study developed an autonomous navigation system using Fuzzy Cognitive Maps (FCM). Fuzzy Cognitive Map is a tool that can model qualitative knowledge in a structured way through concepts and causal relationships. Its mathematical representation is based on graph theory. A new variant of FCM, named Event Driven-Fuzzy Cognitive Maps (ED-FCM), is proposed to model decision tasks and/or make inferences in autonomous navigation. The FCM's arcs are updated from the occurrence of special events as dynamic obstacle detection. As a result, the developed model is able to represent the robot's dynamic behavior in presence of environment changes. This model skill is achieved by adapting the FCM relationships among concepts. A reinforcement learning algorithm is also used to finely adjust the robot behavior. Some simulation results are discussed highlighting the ability of the autonomous robot to navigate among obstacles (navigation at unknown environment). A fuzzy based navigation system is used as a reference to evaluate the proposed autonomous navigation system performance.

An energy‐efficient scheme for reporting events over WSNs

PurposeEnergy consumption has always been the most serious issue to consider while deploying wireless sensor networks (WSNs). Sensor nodes are limited in power, computational capacities and memory so reporting the occurrence of specific events, such as fire or flooding, as quickly as possible using minimal energy resources is definitely a challenging issue. The purpose of this paper is to propose a new, reactive and energy‐efficient scheme for reporting events. In this scheme, nodes that detect a certain event will organize themselves into a cluster, elect a clusterhead that will collect data from the cluster members, aggregate it and forward it to the mobile sink.Design/methodology/approachIn order to evaluate the scheme, a new sensor node model was designed, where the network layer is implemented from scratch. This layer contains the state process model of the algorithm which was made available through a high‐fidelity process modeling methodology.FindingsSimulation results show that a high‐event notification delivery ratio and a significant energy saving is achieved by deploying the proposed sensor node model; comparisons with existing methods show the efficiency of using the new scheme.Originality/valueThe new contribution in this paper is a novel, reactive and energy‐efficient scheme for reporting events over WSNs. The concept introduced in this paper will decrease energy consumption inside the network and, thus, improve its lifetime.

Gene Expression Patterns Related to Osteogenic Differentiation of Bone Marrow–Derived Mesenchymal Stem Cells DuringEx VivoExpansion

Bone marrow is commonly used as a source of adult multipotent mesenchymal stem cells (MSCs), defined for their ability to differentiate in vitro into multiple lineages. The ex vivo-expanded MSCs are currently being evaluated as a strategy for the restoration of function in damaged skeletal tissue, both in cell therapy and tissue engineering applications. The aim of this study was to define gene expression patterns underlying the differentiation of MSCs into mature osteoblasts during the expansion in vitro, and to explore a variety of cell functions that cannot be easily evaluated using morphological, cytochemical, and biochemical assays. Cell cultures were obtained from bone marrow samples of six individuals undergoing total hip replacement, and a large-scale transcriptome analysis, using Affymetrix HG-U133A Plus 2.0 array (Affymetrix((R)), Santa Clara, CA), was performed at the occurrence of specific events, including the appearance of MSC surface markers, formation of colonies, and deposition of mineral nodules. We focused our attention on 213 differentially upregulated genes, some belonging to well-known pathways and some having one or more Gene Ontology annotations related to bone cell biology, including angiogenesis, bone-related genes, cell communication, development and morphogenesis, transforming growth factor-beta signaling, and Wnt signaling. Twenty-nine genes, whose role in bone cell pathophysiology has not been described yet, were found. In conclusion, gene expression patterns that characterize the early, intermediate, and late phases of the osteogenic differentiation process of ex vivo-expanded MSCs were defined. These signatures represent a useful tool to monitor the osteogenic process, and to analyze a broad spectrum of functions of MSCs cultured on scaffolds, especially when the constructs are conceived for releasing growth factors or other signals to promote bone regeneration.