Explicit Causality Research Articles

Risk Chain Mining of Hazard Sources in Metro Operation System Safety: A New Method to Mine and Control Risk for Safety Management

With the promotion of the major strategy of national transportation power, the super-large-scale metro operation network has become an inevitable trend, and operation safety has become increasingly prominent. Metro operation dispatch logs and accident reports were taken as the research object, the hazard sources were efficiently and accurately identified, the risk chains of hazard sources were mined, and the risk evolution mechanism was revealed. Firstly, the transportation lexicon was constructed to improve the accuracy of word segmentation, the text features were extracted based on the term frequency-inverse document frequency (TF-IDF) algorithm, and the key eigenvalues were mined to identify the key hazard sources. Secondly, pattern matching was used to extract explicit causality, and the Self-Attention BiLSTM extracted implicit causality and integrated event trigger word position identification to enhance the effectiveness of implicit causality extraction. Finally, an example was given to verify the efficiency and accuracy of the model, the experiments showed that the F value of extraction effect was increased by nearly 10%, the extraction accuracy was 87.53%, the identification of operational hazard sources was more accurate, and the visualization of risk evolution law was realized.

Tactical mission event logic graph construction for Network-Centric Warfare

Constructing a Tactical Mission Event Logic Graph (TMELG) can assist military staff to analyze the composition and evolution process of tactical missions. We can construct Military Internet of Things (MIoT) applications based on the TMELG to assist military staff to make planning and study tactical missions in the Network-Centric Warfare situation. In this paper, we proposed a representation framework for tactical mission events, based on the Simple Event Model (SEM), to present and construct the TMELG. First of all, sub-mission events and event elements are extracted from military texts with an invented Two-Stage Event Extraction (TSEE) method. Secondly, the Hierarchical Task Network (HTN) was applied to decompose a tactical mission into sub-missions, which were further decomposed into atomic missions, then basic relations were established for sub-missions. Finally, we exploited the explicit causality among sub-mission events, and the implicit causality between atomic mission events and military resources with the Granger Causality Model (GCM). With respect to automatic extraction of sub-mission events and event elements from military texts, the experimental results showed that our TSEE method outperforms the listed methods. The successful application of TMELG to a joint landing operation case verifies the feasibility of our construction method proposed in this paper.

A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports.

Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents. However, it is a complicated work to find causality among events in a CEEG. This paper presents a method to accurately extract event causality by using a neural network and structural analysis. First, we identify the events and their component elements from fault trees by natural language processing technology. Then, causality in accident events is divided into explicit causality and implicit causality. Explicit causality is obtained by analyzing the hierarchical structure relations of event nodes and the semantics of component logic gates in fault trees. By integrating internal structural features of events and semantic features of event sentences, we extract implicit causality by utilizing a bidirectional gated recurrent unit (BiGRU) neural network. An algorithm, named CEFTAR, is presented to extract causality for safety events in chemical accidents from fault trees and accident reports. Compared with the existing methods, experimental results show that our method has a higher accuracy and recall rate in extracting causality.

Problematizations in Assisted Dying Discourse: Testing the “What's the Problem Represented to Be?” (WPR) Method for Critical Health Communication Research

In this paper I test the methodological potential of a set of six questions, called the ‘What’s the Problem Represented to be?,’ or WPR approach, borrowed from the field of discursive policy analysis (Bacchi 2012) for doing critical health communication (CHC) research. WPR is generative for critical health researchers because it shares the goal of challenging implicit and explicit causality and correlation within discourse. I apply these questions to examine the case of legalized medical assistance in dying (MAiD) in Canada, arguing that their simplicity and capaciousness facilitate critical responses to care disparities. This particular case is edifying for CHC researchers, because ideals of good living, ‘dignified’ death, and the role of medicine are all crystallized within legal, media, and medical discourse on assisted dying. Debating what death can or should look like depends on a clearer understanding of how the logics of embodied difference (especially ability, race, and gender) are always already stacking the odds against equal political participation in, and access to health. The WPR approach flips the ‘proper objects’ of health-centered critical research, beginning by scrutinizing proposed solutions in order to rethink the problems implicit therein. Asking such questions as “How has this representation of the ‘problem’ come about?”, “What effects are produced by this representation of the ‘problem’?” and “How has it been (or could it be) questioned, disrupted and replaced?” productively situates the researcher between the discursive practices governing health and health care praxis itself. Ultimately, I argue that the WPR method encapsulates some of the best critical perspectives from the corpus of CHC research praxis, making it a helpful tool for advancing health communication research.

Theatre and Narrative: the Example of Contemporary Monologic Plays

Recent decades have seen a real vogue for solitary speeches in theatre (monologues or soliloquies) spoken by a character speaking and telling stories, alone on stage. These long verbal streams throw into question the main principles of dramatic plays (unity of the plot, ordered and completed progression of the actions, chronological development and explicit causality of sequencing). In the first instance, I will show how these texts are indeed true narratives since the fable, similar in this respect to fictional works, is no longer limited to a temporal development that is strictly chronological. In the second instance, I will show that the solitude of the character is not necessarily anti-dramatic because these monologues are highly polyphonic, bringing many different characters into play. This is a sign of a process that has been under way for several years, bringing theatre closer to the novel.

Cross-linguistic activation of implicit causality biases in Korean learners of English

This study investigates how the strength of referential biases associated with implicit vs explicit causality predicates in Korean affects Korean-speaking learners’ reference choices in English. Sentence-completion experiments with Korean (Experiment 1a) and English (1b) native speakers showed that Korean speakers referred to the subject more following predicates with explicit vs implicit causality marking, whereas English speakers showed no difference in referential bias for the English translation correspondents of these predicates, which did not contain explicit causality marking. In Experiment 2, Korean learners of English completed an English sentence-completion task, either preceded or followed by a translation task, to test whether strength of referential bias in Korean would affect their referential choices in English. After factoring in individual differences in cross-linguistic associations, results provided evidence that cross-language activation at the word level affects reference processing at a discourse level, with the predicted effect somewhat enhanced by translation priming.

Causality in social life cycle impact assessment (SLCIA)

The social life cycle impact assessment (SLCIA) incorporates either a type I or type II characterization model. We improved both models by introducing explicit causality by using statistic modeling through development of (1) a quantitative approach to simultaneously identify impact pathways of type II models with multiple impact categories, targeting SLCIA method developers and (2) a new hybrid model to establish causality between inventory indicators and subcategories, targeting social life cycle assessment practitioners. Causality establishments for type II impact pathways and the new hybrid model are the core requirements for this study. We used structural equation modeling (SEM) to identify the impact pathways for type II characterization models, therefore resolving the issues of unobservability and unvalidatibility in type II models. Using country-level data from the World Bank, the method was applied to an example impact pathway at macro-scale. We applied Bayesian networks in our hybrid model to address the issues of relevance and representativeness in type I models, assuming the unobservable social performances of an organization are the causes for observable inventory indicators. The method was applied to a hypothetical example for the stakeholder of the worker at company scale. Temporal precedence (i.e., lag effects) was incorporated into both models. The results from the confirmatory SEM supported our hypotheses that comprised the impact pathway from economic development to health outcomes, which were fully mediated by health expenditures and health access. A 1-year lag between each impact category resulted in the best model fit. Limitations on the data as well as subjective choice of indicators to represent impact categories are subject to criticism. The results from the hybrid model showed that, depending on the likelihood of the inventory indicators, the posterior probability of subcategories either deviated from their prior probability or behaved similarly. The construction of proper conditional probability tables and the choice of probability distribution for the likelihood are major challenges for the hybrid model. This study was the first attempt in using statistic causal models to quantitatively identify unobservable impact pathways of the type II model and to develop a hybrid model for SLCIA. A SEM that incorporates temporal precedence enables identification of impact pathways with multiple unobservable impact categories. The hybrid model using Bayesian networks represents the subcategories in posterior probabilities instead of absolute scores, helping companies to better develop instructions for future management practices.

Granger causality, exogeneity, cointegration, and economic policy analysis

Policy analysis had long been a main interest of Clive Granger’s. Here, we present a framework for economic policy analysis that provides a novel integration of several fundamental concepts at the heart of Granger’s contributions to time-series analysis. We work with a dynamic structural system analyzed by White and Lu (2010) with well defined causal meaning; under suitable conditional exogeneity restrictions, Granger causality coincides with this structural notion. The system contains target and control subsystems, with possibly integrated or cointegrated behavior. We ensure the invariance of the target subsystem to policy interventions using an explicitly causal partial equilibrium recursivity condition. Policy effectiveness is ensured by another explicit causality condition. These properties only involve the data generating process; models play a subsidiary role. Our framework thus complements that of Ericsson et al. (1998) (EHM) by providing conditions for policy analysis alternative to weak, strong, and superexogeneity. This makes possible policy analysis for systems that may fail EHM’s conditions. It also facilitates analysis of the cointegrating properties of systems subject to policymaker control. We discuss a variety of practical procedures useful for analyzing such systems and illustrate with an application to a simple model of the US macroeconomy.

Granger Causality, Exogeneity, Cointegration, and Economic Policy Analysis

Policy analysis had long been a main interest of Clive Granger’s. Here, we present a framework for economic policy analysis that provides a novel integration of several fundamental concepts at the heart of Granger’s contributions to time-series analysis. We work with a dynamic structural system analyzed by White and Lu (2010) with well de ned causal meaning; under suitable conditional exogeneity restrictions, Granger causality coincides with this structural notion. The system contains target and control subsystems, with possibly integrated or cointegrated behavior. We ensure the invariance of the target subsystem to policy interventions using an explicitly causal partial equilibrium recursivity condition. Policy effectiveness is ensured by another explicit causality condition. These properties only involve the data generating process; models play a subsidiary role. Our framework thus complements that of of Ericsson, Hendry, and Mizon (1998) (EHM) by providing conditions for policy analysis alternative to weak, strong, and super-exogeneity. This makes possible policy analysis for systems that may fail EHM’s conditions. It also facilitates analysis of the cointegrating properties of systems subject to policymaker control. We discuss a variety of practical procedures useful for analyzing such systems and illustrate with an application to a simple model of the U.S. macroeconomy.

Causal signal transmission by quantum fields. III: Coherent response of fermions

Structural response properties of fermionic fields are investigated. In the presence of fermions the key technical concept becomes response combination, or R-normal product, of field operators. It generalises the notion of time-normal operator product to response problems. Time-normal products are a special case of R-normal products without inputs; this paper thus also generalises the concept of time-normal ordering to fermions. Explicit causality of R-normal products of arbitrary (bosonic and/or fermionic) field operators is proven, and explicit relations expressing them by conventional Green’s functions of quantum field theory are derived.

An Investigation as to the Cause of Beam Asymmetry in a Compact Gas Discharge Ion Source: A Focus on Beam-Wall Interaction

Helium beams in a compact gas discharge source lose their transverse symmetry when the extracting electrode is biased at high potentials. Further, this condition gives rise to excessive formation of electrons within the vicinity of the beam line of propagation. A plausible explanation for these abnormalities is explained via the effects of the source extractor's lens property on the beams' physical configuration. The optical relation of the extractor shows that when the extraction voltage (Ve) exceeds half the discharge voltage (Vd), its focal length extends backward pass the discharge region's emitting orifice. As a consequence, beam divergence is increased akin to light beams expanding when the source is positioned between a negative lens and its principal focus. Numerical simulations of the beams' envelopes at different discharge and biasing conditions provide further proof of the theory's validity. When Ve>Vd/2, the construct shows exiting beams to have waists greater than the diameter of the drift tube suggesting increased interaction between beam edge and the tube's interior walls resulting in secondary electron emissions. The presence of electrons inexorably leads to charge neutralization thus creating asymmetric beams downstream. Mass spectroscopic detection of O- ions most likely from surface oxides, and increased electron densities obtained by way of Langmuir measurements are phenomenological evidences to this effect. This work intends to establish the explicit causality relation between secondary electron emission and the formation of asymmetric beams in miniaturized ion sources.

The Method of Relaxed Causality in the Bond Graph Analysis of Nonlinear Systems

A method is presented for the formulation of mathematical models of nonlinear systems. The method employs as the underlying principle the relaxation of the requirement of explicit causality at 0- and 1-junctions. Instead, causal assignments are made which are consistent with the computing causality of the nonlinear elements, thus leading to causal violations in the conventional sense. A classification of causal violations is identified which provides information useful in the systematic derivation of algebraic equations. A most attractive aspect of the method is the fact that causal relaxation is required only when needed, and only to the extent needed.