Triggering Event Research Articles

Network control based load frequency control considering the transmission delay

AbstractThis study introduces a network‐based controller to regulate frequency within power systems. The primary objective of this investigation is to ensure the stability of power systems experiencing delays in control signal transmission. Initially, a modified networked predictor is proposed to anticipate future signals within a decentralized load frequency control framework. Subsequently, a timestamp technique is employed to actively compensate for the impact of transmission delays. Additionally, an event trigger mechanism is incorporated to complement the extended state observer and optimize signal transmission resource usage. The outcomes of the study indicate that the proposed method effectively mitigates delay effects while maintaining superior performance. Moreover, the proposed method remarkably reduces the required transmission resources.

Reconstruction of the Dynamics of a Catastrophic Crater Lake Outburst Flood, Changbaishan‐Tianchi Volcano

AbstractReconstruction of the catastrophic drainage following the Millennium Eruption (ME) of Changbaishan‐Tianchi volcano in 946 ± 20 CE is of great significance, as it contributes to improving the regional maximum flood record and develop rare flood risk analysis. However, limited knowledge exists concerning the failure mode, magnitude, and transport processes of the outburst flooding. In this work, we present a whole system model that describes the paleohydrology of catastrophic drainage using geological records along the downstream valley. The model encompasses the crater lake dynamics, an approximation of the breach erosion process and flood propagation downstream. The boulder competence method was used to constrain by reasonable flow parameters, while mitigating the uncertainty caused by the ambiguous geological paleostage indicators. Paleohydrologic analysis indicates that at least 1 km3 of water was released from the caldera, with the vertical breach erosion rates as high as 34 m/hr. Volcanic activity during the ME II may have directly contributed to triggering of the flood event. The local hydrodynamic response of the downstream riverbed captures the dynamic migration patterns of sediments across spatio‐temporal scales, offering a comprehensive interpretation of the specific scouring surfaces observed in the geological profile. The analysis of simulated inundation boundaries reveals that not all recorded inundations can be attributed to the crater lake outburst event. Reconstructions of megafloods based on downstream constraints on flood stage, velocity and discharge can help to infer and constrain the dynamics of dam failure mechanisms, and also contribute to our understanding of these complex paleohydrologic events.

A Composite Vibration Control Strategy for Active Suspension System Based on Dynamic Event Triggering and Long Short-Term Memory Neural Network

A Composite Vibration Control Strategy for Active Suspension System Based on Dynamic Event Triggering and Long Short-Term Memory Neural Network

Triggering dark showers with conditional dual auto-encoders

Abstract We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g. in the real-time event triggering systems of large hadron collider experiments such as ATLAS and CMS.

Triggers for self-regulated learning: A conceptual framework for advancing multimodal research about SRL

This paper introduces a theory-driven trigger regulation framework for advancing multimodal analytical approaches to research about self-regulated learning. Events and/or situations that may inhibit learning processes and, thus, require regulatory responses are defined as trigger events. Empirically identifying trigger signals in multimodal data as markers for the regulation of cognition, motivation, emotion, and behavior has great potential for advancing the field. We propose a trigger regulation framework and explain how it can be leveraged in multimodal research for detecting trigger signals focusing analysis on meaningful regulatory responses. This conceptual framework offers potential to guide methodological and analytical advances in research to examine the situated nature of regulatory responses and within-person individual differences in SRL as they play out during complex task work and teamwork. Educational relevance and implications statementThe trigger regulation framework contributes to advancing multimodal approaches to the study of SRL. It presents a theory driven analytical approach for detecting, modeling, and interpreting adaptive and maladaptive regulation during individual or collaborative work. Grounding analytical approaches to multimodal data analysis in this framework has potential to increase the quality and accuracy of research findings and interpretations and inform the development of interventions and AI systems.

Issue Fields and Echo Chambers: Increasing Field Contestation Fueled by Moral Emotions

We investigate how issue fields with increasing levels of contestation can develop into fields characterized by echo chambers. Studying the introduction of a controversial new approach to addiction services – harm reduction – we explain how proponents’ and opponents’ rhetorical arguments changed over time, transitioning the issue field through different configurations. Our findings reveal how field actors were initially differentiated by moral convictions, and as their expression of moral emotions became more intense, the two groups became increasingly divided and polarized in their views, leading to an issue field characterized by echo chambers. Through our analysis of archival materials and interview data, we explicate this process by identifying three phases of issue field transition: (1) Creating a moral emotional divide; (2) Intensifying antagonization; (3) Insulating against the other side. We contribute to the literature by presenting a model of change explaining how emotional rhetoric, together with different types of triggering events, can fuel increasing levels of contestation and drive the field toward developing echo chambers. Second, by taking a discursive view of issue fields with particular attention to rhetorical arguments, we provide foundational work for an institutional perspective on echo chamber – that echo chambers result from ongoing social processes where people encapsulate themselves based on a sense of right and wrong, in contrast to the predominant view of becoming trapped in an enclosed space. Third, through our focus on the role of moral emotions and how they can escalate in situations of contestation, we advance knowledge regarding the importance of emotions in field dynamics.

Event‐triggered optimal control based on prescribed performance for a two‐link robotic manipulator

SummaryIn this article, an event‐triggered optimal control method based on prescribed performance is investigated for a constrained two‐link robotic manipulator system. To satisfy the performance constraints of the system, an equated error model of the dynamics model is established by means of two auxiliary functions and the prescribed performance technique. A predictive controller based on an adaptive event triggering mechanism is obtained by solving a constraint optimization problem for this transformed error model, and the triggering threshold can be adjusted based on real‐time changes of the system. This controller realizes the tracking of the joint angle with the desired angle and meets the prescribed performance conditions. Finally, the control algorithm is shown to be an effective method for improving control performance through numerical simulations and comparison with other prescribed performance functions.

Document-Level Event Argument Extraction with Sparse Representation Attention

Document-level Event Argument Extraction (DEAE) aims to extract structural event knowledge composed of arguments and roles beyond the sentence level. Existing methods mainly focus on designing prompts and using Abstract Meaning Representation (AMR) graph structure as additional features to enrich event argument representation. However, two challenges still remain: (1) the long-range dependency between event trigger and event arguments and (2) the distracting context in the document towards an event that can mislead the argument classification. To address these issues, we propose a novel document-level event argument extraction model named AMR Parser and Sparse Representation (APSR). Specifically, APSR sets inter- and intra-sentential encoders to capture the contextual information in different scopes. Especially, in the intra-sentential encoder, APSR designs three types of sparse event argument attention mechanisms to extract the long-range dependency. Then, APSR constructs AMR semantic graphs, which capture the interactions among concepts well. Finally, APSR fuses the inter- and intra-sentential representations and predicts what role a candidate span plays. Experimental results on the RAMS and WikiEvents datasets demonstrate that APSR achieves a superior performance compared with competitive baselines in terms of F1 by 1.27% and 3.12%, respectively.

The ATLAS RPC Phase-II upgrade for High Luminosity LHC era

Resistive Plate Chambers (RPC) detectors play a crucial role in triggering events containing muons in the central region of ATLAS. In view of the High Luminosity-LHC program, the existing RPC system, consisting of six independent cylindrical detector layers each providing a full space time localization of hits, is currently undergoing a significant upgrade. In the next few years, 306 triplets of new generation RPCs will be installed in the innermost region of the ATLAS Muon Barrel Spectrometer, increasing from 6 to 9 the number of tracking layers, doubling the trigger lever arm. This allows a substantial enhancement of the present trigger redundancy, increasing the coverage from 76% to approximately 96%. Fitting new chambers in the narrow space left in ATLAS inner barrel was a challenge, achieved by optimizing RPC materials and thickness, featuring a 1 mm gas gap (instead of 2 mm), and 1.4 mm resistive electrodes (instead of 1.8 mm) while reaching an high rate capability. To achieve such results, a 100 ps precise Time-to-Digital Converter (TDC) has been integrated in the front-end electronics ASIC. The expected time resolution of a single 1 mm RPC gas gap is approximately 300 ps, and the possibility of a stand-alone Time of Flight measurement will have a huge impact on ATLAS searches for massive long-lived particles. An overview and the present status of the ATLAS RPC Phase-II project will be presented.

Functional and Structural Changes in Diaphragm Neuromuscular Junctions in Early Aging.

Age-related impairment of the diaphragm causes respiratory complications. Neuromuscular junction (NMJ) dysfunction can be one of the triggering events in diaphragm weaknesses in old age. Prominent structural and functional alterations in diaphragm NMJs were described in elderly rodents, but NMJ changes in middle age remain unclear. Here, we compared diaphragm muscles from young adult (3 months) and middle-aged (12 months) BALB/c mice. Microelectrode recordings, immunofluorescent staining, electron microscopy, myography, and whole-body plethysmography were used. We revealed presynaptic (i) and postsynaptic (ii) changes. The former (i) included an increase in both action potential propagation velocity and neurotransmitter release evoked by low-, moderate-, and high-frequency activity but a decrease in immunoexpression of synapsin 1 and synaptic vesicle clustering. The latter (ii) consisted of a decrease in currents via nicotinic acetylcholine receptors and the area of their distribution. These NMJ changes correlated with increased contractile responses to moderate- to high-frequency nerve activation. Additionally, we found alterations in the pattern of respiration (an increase in peak inspiratory flow and a tendency of elevation of the tidal volume), which imply increased diaphragm activity in middle-aged mice. We conclude that enhancement of neuromuscular communication (due to presynaptic mechanism) accompanied by improved contractile responses occurs in the diaphragm in early aging.

Distributed adaptive event‐triggered consensus of switched nonlinear multi‐agent systems with state and input delays

SummaryIn this article, for switched nonlinear time‐delay multi‐agent systems (MASs) under directed graphs, an adaptive event‐triggered (ET) consensus problem is investigated. Each agent's switching signal is allowed to be arbitrary and asynchronous. To save communication resources, an adaptive ET consensus strategy is designed. In addition, the Pade approximation approach is introduced to transform the system into an input delay‐free system. A Lyapunov–Krasovskii functional is employed to analyze the effect of time‐varying delays on system stability. It is proved that the consensus errors of switched NMASs are bounded, and the Zeno behavior does not exist. Finally, the presented strategy's effectiveness is verified by simulation results.

Distributed zero-gradient-sum optimisation algorithm with an edge-based adaptive event-triggered mechanism

This paper addresses the continuous-time distributed optimisation problem over networks, where the global objective function is formed by a sum of convex local objective functions. To avoid continuous communication among agents, a distributed adaptive Zero-Gradient-Sum (ZGS) optimisation algorithm under a dynamic event-triggered scheme is proposed. This is achieved by dynamically adjusting the coupling strengths of adjacent agents within the network. Our analysis confirms that the proposed algorithm will exponentially converge to the optimal solution provided that the underlying communication graph is undirected and connected. Additionally, we demonstrate that our event-triggered scheme is not subject to Zeno behaviour, which is a theoretical concern in systems with frequent event triggers.

Asynchronous nonfragile guaranteed performance control for singular switched positive systems: An event‐triggered mechanism

SummaryThis paper addresses the guaranteed performance control problem for a class of singular switched positive systems, where the switching signal is subject to a state‐dependent process. Firstly, the causality, regularity, positivity, and asymptotical stability of the considered systems are discussed. In order to prevent the occurrence of data collisions and reduce the consumption of communication resources, the event‐triggered (E‐T) mechanism is applied. This is one of the initial attempts to introduce the E‐T scheme for such special systems. Besides, an asynchronous nonfragile controller under the E‐T scheduling scheme is designed to make certain that the resulting closed‐loop systems are causal, regular, positive, asymptotically stable, and have a guaranteed performance value . Through the application of the co‐positive Lyapunov function method and the min‐projection strategy, the corresponding sufficient conditions are given in the form of linear programming (LP). Finally, the effectiveness of the controller proposed in this paper is verified via two simulation examples.

Attitude tracking control of a foldable wave energy powered AUV based on linear time-varying MPC under position constraints

In this paper, we investigate the tracking control of attitude and specific spatial position for a foldable wave energy powered autonomous underwater vehicle (FWEPAUV) under space constraints. In order to reduce energy consumption and maximizing the efficiency of the charging, a cascaded dual closed-loop control strategy is proposed. Firstly, we utilize model predictive control (MPC) with multi-variable constraint capabilities to design the kinematics controller. The output constraints are set as space constraints for MPC, and an objective function is used to strike a balance between control accuracy and energy consumption. The improved chaotic firefly algorithm (ICFA) is chosen due to its rapid convergence and strong robustness. It is employed in the rolling optimization process of MPC to secure a viable solution. Secondly, the kinematics controller is combined with the dynamics controller based on adaptive sliding mode control (ASMC) to achieve robust tracking control. In order to eliminate the interference from time-varying ocean currents and complex external forces meanwhile utilizing these interferes to reduce energy consumption, An ocean current observer based on ICFA-PI control and a prescribed time disturbance observer (PTDO) are devised to estimate them. Finally, an event-triggered (ET) mechanism is applied to integrate with the trajectory tracking controller to decrease the computational load. The stability of the ICFA-MPC controller and the ASMC controller is proven using the Lyapunov stability theory. Simulation results demonstrate that in complex external interference environments, the controller can achieve stable tracking control of the attitude and a specific spatial position, thereby meeting predefined goals such as space constraints, energy savings, and maximizing the efficiency of the charging.

Interactions for Socially Shared Regulation in Collaborative Learning: An Interdisciplinary Multimodal Dataset

Socially shared regulation plays a pivotal role in the success of collaborative learning. However, evaluating socially shared regulation of learning (SSRL) proves challenging due to the dynamic and infrequent cognitive and socio-emotional interactions, which constitute the focal point of SSRL. To address this challenge, this article gathers interdisciplinary researchers to establish a multimodal dataset with cognitive and socio-emotional interactions for SSRL study. Firstly, to induce cognitive and socio-emotional interactions, learning science researchers designed a special collaborative learning task with regulatory trigger events among triadic people for the SSRL study. Secondly, this dataset includes various modalities like video, Kinect data, audio, and physiological data (accelerometer, EDA, heart rate) from 81 high school students in 28 groups, offering a comprehensive view of the SSRL process. Thirdly, three-level verbal interaction annotations and nonverbal interactions including facial expression, eye gaze, gesture, and posture are provided, which could further contribute to interdisciplinary fields such as computer science, sociology, and education. In addition, comprehensive analysis verifies the dataset’s effectiveness. As far as we know, this is the first multimodal dataset for studying SSRL among triadic group members.

What Triggers Change in Antimicrobial Use?

SummaryThis article considers how different ‘triggers' contribute to changes in the use of antimicrobials. Drawing on the Triggering Change model, the article suggests that the use of antimicrobials is path dependent: economically, socially and culturally invested in maintaining a steady trajectory, with incremental changes. Triggering events – such as farm succession, financial crisis, or disease outbreak – are required to break these dependencies and stimulate transitions to new farming trajectories. The article investigates which triggering events are significant in the context of responsible antimicrobial usage, in three European countries, spanning the beef, dairy, poultry and pig sectors. Results demonstrated that major reductions in antimicrobial use are often part of larger transition processes. Triggers led to major changes on farm which included reduction in antimicrobial use amongst other changes. When antimicrobial change occurred in isolation, it was typically in response to legislation, and progressed incrementally over time. To achieve major changes in antimicrobial use thus requires policies which work with trigger events such as supporting training of successors, and enabling farmers who have experienced major disease outbreaks to ‘build back better’. Working to shape what farmers understand as ‘good farming’ through education, regulations and benchmarking, are also important options.

Resilient multi-objective mission planning for UAV formation: A unified framework integrating task pre- and re-assignment

Combat effectiveness of unmanned aerial vehicle (UAV) formations can be severely affected by the mission execution reliability. During the practical execution phase, there are inevitable risks where UAVs being destroyed or targets failed to be executed. To improve the mission reliability, a resilient mission planning framework integrates task pre- and re-assignment modules is developed in this paper. In the task pre-assignment phase, to guarantee the mission reliability, probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model. And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution. As in the task-reassignment phase, possible trigger events are first analyzed. A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario. And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention. Three cases of different scales demonstrate that the two modules cooperate well with each other. On the one hand, the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced. On the other hand, the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond. The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.

Observed Global Mean State Changes Modulating the Collective Influence of the Tropical Atlantic and Indian Oceans on ENSO

Abstract In the last decades, many efforts have been made to understand how different tropical oceanic basins are able to impact El Niño–Southern Oscillation (ENSO). However, the collective connectivity among the tropical oceans and their associated influence on ENSO are less understood. Using a complex network methodology, the degree of collective connectivity among the tropical oceans is analyzed focusing on the detection of periods when the tropical basins collectively interact and the Atlantic and Indian basins influence the equatorial Pacific sea surface temperatures (SSTs). The background state for the periods of strong collective connectivity is also investigated. Our results show a marked multidecadal variability in the tropical interbasin connection, with periods of stronger and weaker collective connectivity. These changes seem to be modulated by changes in the North Atlantic Ocean mean state a decade in advance. In particular, strong connectivity occurs in periods with colder than average tropical North Atlantic surface ocean. Associated with this cooling, an anomalous convergence of the vertical integral of total energy flux (VIEF) takes place over the tropical north–west Atlantic, associated with anomalous divergence of VIEF over the equatorial eastern Pacific. In turn, an anomalous zonal surface pressure gradient over the tropical Pacific weakens the trades over the western equatorial Pacific. Consequently, a shallower thermocline emerges over the western equatorial Pacific, which can enhance thermocline feedbacks, the triggering of ENSO events, and therefore, ENSO variability. By construction, our results put forward opposite conditions for periods of weak tropical basin connectivity. These results have important implications for seasonal to decadal predictions.

Effective type label-based synergistic representation learning for biomedical event trigger detection

BackgroundDetecting event triggers in biomedical texts, which contain domain knowledge and context-dependent terms, is more challenging than in general-domain texts. Most state-of-the-art models rely mainly on external resources such as linguistic tools and knowledge bases to improve system performance. However, they lack effective mechanisms to obtain semantic clues from label specification and sentence context. Given its success in image classification, label representation learning is a promising approach to enhancing biomedical event trigger detection models by leveraging the rich semantics of pre-defined event type labels.ResultsIn this paper, we propose the Biomedical Label-based Synergistic representation Learning (BioLSL) model, which effectively utilizes event type labels by learning their correlation with trigger words and enriches the representation contextually. The BioLSL model consists of three modules. Firstly, the Domain-specific Joint Encoding module employs a transformer-based, domain-specific pre-trained architecture to jointly encode input sentences and pre-defined event type labels. Secondly, the Label-based Synergistic Representation Learning module learns the semantic relationships between input texts and event type labels, and generates a Label-Trigger Aware Representation (LTAR) and a Label-Context Aware Representation (LCAR) for enhanced semantic representations. Finally, the Trigger Classification module makes structured predictions, where each label is predicted with respect to its neighbours. We conduct experiments on three benchmark BioNLP datasets, namely MLEE, GE09, and GE11, to evaluate our proposed BioLSL model. Results show that BioLSL has achieved state-of-the-art performance, outperforming the baseline models.ConclusionsThe proposed BioLSL model demonstrates good performance for biomedical event trigger detection without using any external resources. This suggests that label representation learning and context-aware enhancement are promising directions for improving the task. The key enhancement is that BioLSL effectively learns to construct semantic linkages between the event mentions and type labels, which provide the latent information of label-trigger and label-context relationships in biomedical texts. Moreover, additional experiments on BioLSL show that it performs exceptionally well with limited training data under the data-scarce scenarios.

Tracking of charged particles with nanosecond lifetimes at LHCb

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