Triggering Event Research Articles

Digital Transformation of Incumbent Pipeline Firms through Platformization

ABSTRACT Advancements in digital technology necessitate a shift from traditional linear buyer-supplier value chains to a network-centric approach facilitated by digital platforms. Despite digital platforms being a dominant model for transformation, limited research has explored the enablers for incumbent pipeline firms to embrace platformization. To address this gap, this study reviews existing literature on digital transformation, identifying nine enablers for digital platformization: changing client behavior, information technology (IT) capabilities development, structural efficiencies, disruptive competitive forces, scope advantages, economic triggers, disruptive third-party technology, creation of autonomous corporate structures, and regulatory compliance. Case studies of platformization in diverse industries are analyzed using the modified total interpretive structural modeling (m-TISM) approach, resulting in a hierarchical model with five levels. This model delineates interrelationships among trigger events, external enablers, internal organizational enablers, process enablers, and outcome drivers. Furthermore, the study highlights that the creation and extraction of value from digital platforms are operationalized through e-business strategies, contributing insights to e-business literature by discussing the implications of these enablers on dimensions such as commerce, collaboration, communication, connection, and computing.

Psychological Stressor Of End Stage Chronic Kidney Disease Patients On Dialysis. A Battle For Life

Introduction Patients with chronic kidney disease (CKD) may choose to undergo dialysis. Factors that may have led patients to prescribe psychological interventions related to dialysis are poorly understood in the literature. The purpose of this study was to explore multi-level factors surrounding dialysis modalities such as Diagnosed Mood Disorders, Existential crises, Triggering events, Social support, and Distrust towards the process of dialysis.ObjectivesThe study aims to investigate the psychological battle of the client while going through the process of dialysis. The study reveals multiple mood disorders and existential crises leading to depression among chronic kidney disease patients. Therefore the study was conducted with the aim of providing a therapeutic guide line in future once the factors are investigated in detail.MethodsSemi-structured qualitative interviews were conducted in a dialysis clinic in Karachi where 19 participants participated in this qualitative study. The age ranges from 40-76. Initiating with informed consent followed by surveys assessing demographic and clinical information were administered to participants following their interviews.ResultsQualitative findings suggested that patients were dealing with Clinical Mood Disorders without being provided treatment. Moreover, the cohesive family support enabled them to continue with daily living activities; however, the patients with low support triggering adverse events in life lost their lives in follow-up sessions. Furthermore, nephrology care doesn’t seem sufficient as they are dealing with existential crises of hopelessness, regret, condemnation, and elevated death anxiety. In CKD the misinterpretation of dialysis by cognitively substituting it as End of life increased the clinical symptoms of Mood disorders. Thus the risk factors increase disturbing the quality of life.ConclusionsFindings point to broader factors affecting dialysis modalities with Mood disorders. The low social support and adverse triggering events precipitate the risk factors of dialysis treatment. Furthermore, distrust towards dialysis and existential crisis are recommended for therapeutic interventionsDisclosure of InterestNone Declared

The Calorimetric Electron Telescope (CALET) on the International Space Station: Results from the first eight years on orbit

The Calorimetric Electron Telescope, CALET, is an astroparticle physics mission installed on the International Space Station, ISS. The primary objective of the mission is studying the details of galactic cosmic-ray acceleration and propagation, and searching for the possible nearby sources of high-energy electrons and dark matter signatures. The CALET experiment measure the flux of cosmic-ray electrons (including positrons) to 20 TeV, gamma-rays to 10 TeV and nuclei to 1000 TeV. The detector is an all-calorimetric instrument with a total vertical thickness of 30 radiation lengths and fine imaging capability, optimized for the measurement of the electron and positron (all-electron) spectrum well into the TeV energy region. It consists of a charge detector (CHD) with two layers of segmented plastic scintillators for the identification of cosmic-rays via a measurement of their charge over the range Z=1∼40, a 3 radiation length thick tungsten-scintillating fiber imaging calorimeter (IMC) and a 27 radiation length thick lead-tungstate calorimeter (TASC). The instrument was launched on August 19, 2015 to the ISS and installed on the Japanese Experiment Module-Exposed Facility. Since the start of operation in October, 2015, CALET has been collecting scientific data without any major interruption for more than eight years. The number of triggered events over 10 GeV is nearly 1.97 billion events as of November 30, 2023. In this paper, we present the results of the CALET mission so far, including the all-electron energy spectrum, the spectra of protons and other nuclei, gamma-ray observations, as well as the characterization of on-orbit performance. Some results on the electromagnetic counterpart search for LIGO/Virgo gravitational wave events and the observations of solar modulation and gamma-ray bursts are also included.

Event-Triggered-Based Adaptive NN Cooperative Control of Six-Rotor UAVs With Finite-Time Prescribed Performance

This paper studies the disturbance-observer-based adaptive neural network cooperative event-triggered control problem for six-rotor unmanned aerial vehicles with finite-time prescribed performance. Six-rotor unmanned aerial vehicle systems are divided into the position subsystem and the attitude subsystem. The switching threshold event-triggered mechanism that considers the influence of event triggering on the consensus control performance is designed of each six-rotor unmanned aerial vehicle to save the transmission resources, which excludes the Zeno behavior. Neural networks are introduced for estimating the uncertainties and solving the algebraic loop problem. In addition, for the position subsystem, by combining the prescribed performance with the velocity function, a finite-time control scheme is proposed, which can guarantee that the consensus errors converge to a prespecified neighborhood of the origin, and all closed-loop signals are bounded. Based on the designed control input signal of position subsystem, an adaptive neural network event-triggered control mechanism is designed to stabilize the attitude subsystem. Finally, some verification results are given to test the rationality of the proposed control strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The purpose of this paper is to design an event-triggered cooperative control scheme to save the transmission resources for six-rotor unmanned aerial vehicle systems with performance limitations and lumped disturbances. In practical applications, if the impact of the event-triggered mechanism on the system performance is not taken into account, the system performance may be significantly degraded while the information transmission resource is saved. Thus, this paper designs an event-triggered mechanism that considers the system performance, which can effectively weigh the relationship between system performance and transmission resource consumption and better meet the practical requirements. Moreover, a finite-time specified performance control strategy is introduced to avoid the problem of difficult determination of residual set and improve the transient-state and steady-state performances of unmanned aerial vehicle systems, which is more in line with the actual demand.

Ocrelizumab B cell depletion has no effect on HERV RNA expression in PBMC in MS patients

BackgroundEpstein barr virus (EBV) infection of B cells is now understood to be one of the triggering events for the development of Multiple Sclerosis (MS), a progressive immune-mediated disease of the central nervous system. EBV infection is also linked to expression of human endogenous retroviruses (HERVs) of the HERV-W group, a further risk factor for the development of MS. Ocrelizumab is a high-potency disease-modifying treatment (DMT) for MS, which depletes B cells by targeting CD20. ObjectivesWe studied the effects of ocrelizumab on gene expression in peripheral blood mononuclear cells (PBMC) from paired samples from 20 patients taken prior to and 6 months after beginning ocrelizumab therapy. We hypothesised that EBV and HERV-W loads would be lower in post-treatment samples. MethodsSamples were collected in Paxgene tubes, subject to RNA extraction and Illumina paired end short read mRNA sequencing with mapping of sequence reads to the human genome using Salmon and differential gene expression compared with DeSeq2. Mapping was also performed separately to the HERV-D database of HERV sequences and the EBV reference sequence. ResultsPatient samples were more strongly clustered by individual rather than disease type (relapsing/remitting or primary progressive), treatment (pre and post), age, or sex. Fourteen genes, all clearly linked to B cell function were significantly down regulated in the post treatment samples. Interestingly only one pre-treatment sample had detectable EBV RNA and there were no significant differences in HERV expression (of any group) between pre- and post-treatment samples. ConclusionsWhile EBV and HERV expression are clearly linked to triggering MS pathogenesis, it does not appear that high level expression of these viruses is a part of the ongoing disease process or that changes in virus load are associated with ocrelizumab treatment.

Event‐triggered differential private distributed fusion estimation for sensor networks with unknown cross‐correlations

AbstractRecently, preserving privacy and reducing energy consumption become important concerns of sensor networks. However, existing privacy‐preserving estimation methods cause many needless data transmissions, which results in energy waste. Besides, these methods generates fusion estimates with heavy computational burdens, due to needing calculating cross‐covariance matrices of each pair of local estimation errors. Worse still, these cross‐covariances are often unavailable in actual sensor networks. In view of these problems, a novel framework of event‐triggered (ET) differential private distributed fusion estimation is proposed. Within this framework, an ET mechanism is designed to schedule transmissions of local estimates for reducing communication consumptions. Then, with using local perturbation mechanism, conditions for achieving differential privacy and dealing with eavesdroppers which can eavesdrop and fuse local estimates are provided. Based on covariance intersection fusion rule, an ET differential private fusion method is developed, which can guarantee fusion results uniformly stable in spite of completely unknown cross‐covariances. Finally, simulation results verify that the proposed method can preserve privacy and reduce communication consumptions without using cross‐covariances, at the cost of only slight decline of estimation performance.

Unlocking Amides: A General Method for the Self-Immolative Release of Amide-Containing Molecules.

The controlled liberation of molecules from a constructed framework is a subject of profound interest across various chemical fields. It allows for the masking of a molecule's properties and precise deployment upon a single controllable release event. While numerous methodologies have been developed for amines, alcohols, and thiols, approaches for utilising amides as payload-release handles are still in their early stages of development, despite the prevalence of amides in therapeutic compounds and materials. Herein, is presented a comprehensive strategy for the controlled and selective release of a diverse range of amides with stable linkers. The versatility of this approach is demonstrated by its successful application in the targeted release of various amide-containing drugs in their natural form via the use of commonly used trigger motifs, such as dipeptides or glycosides. As a proof of concept, the FDA-approved antibiotic linezolid has been successfully converted into a prodrug form and released selectively only in the presence of the trigger event. Significantly, in its prodrug state, no activity against Mycobacterium tuberculosis was exhibited. Linezolid's full potential was achieved only upon controlled release, where an equipotent efficacy to the free linezolid control was demonstrated, thus emphasising the immense potential of this method.

The event‐driven social responsibility in small and medium‐sized enterprises

AbstractSince the survival and growth of small firms depend largely on their ability to acquire legitimacy within the institutional environment, social responsibility plays an important role in their lives. The aim of this study was to investigate how a disruption in the normal course of business can lead to an evolution and change in the social responsibility activities of small and medium‐sized enterprises. Such disruptions, called critical incidents, interrupt the normal functioning of the business. The case‐based exploratory study found that such critical events can have a positive impact. In particular, they can initiate the integration of socially responsible business practices. This is a journey from philanthropic donations to fully integrated socially responsible practices, often driven by internal triggering events such as organisational growth, unanticipated changes in management, or generational change, which allow new values to be brought on board. When such an internal event is followed by a highly critical but external event, such as the COVID‐19 pandemic, the transformation gains momentum. The findings also suggest that social responsibility can be a new source of competitive advantage.

A novel distributed bearing‐only target tracking algorithm for underwater sensor networks with resource constraints

AbstractUnderwater sensor networks hold immense potential for advancing the field of underwater target tracking, yet they encounter significant resource constraints stemming from energy storage and communication methods. In order to balance tracking accuracy and energy consumption, the authors present a distributed bearing‐only target tracking algorithm that can be used in underwater sensor networks with resource constraints. Anchored in the diffusion cubature information filter framework, this algorithm achieves fusion for non‐linear bearing measurements and state estimation. During the incremental update stage, individual nodes leverage the Posterior Cramer‐Rao Lower Bound as a metric for tracking performance. Subsequently, a strategy for selecting neighbouring nodes is introduced, ensuring tracking accuracy while efficiently kerbing energy consumption. In the diffusion update stage, a multi‐threshold event triggering mechanism is employed to partially diffuse the intermediate estimation. Additionally, an adaptive convex combination weight is proposed for cases involving partial diffusion. Through theoretical analysis, the asymptotic unbiasedness and convergence of the algorithm have been proven. Through Monte Carlo simulation experiments, the authors verify that the algorithm is superior to existing algorithms. Furthermore, the algorithm significantly reduces energy consumption in information interaction, minimising tracking accuracy loss.

Event type induction using latent variables with hierarchical relationship analysis

The conventional approach to event extraction requires predefined event types and their corresponding annotations to train event extractors. However, these prerequisites are often difficult to satisfy in real-world applications. To automatically induct event types, most work has been devoted to clustering event triggers, where a cluster of event triggers is represented as an event type. Some works use trigger semantics, while others use co-occurrence relationships to cluster triggers. However, the clustering results of event triggers obtained by the above work are not sufficiently detailed in describing event types, making it difficult to accurately determine the corresponding event types manually. This paper proposes an open-domain event type induction framework that automatically discovers a set of event types from a given corpus. Unlike previous work on event trigger clustering, this paper takes into consideration the hierarchical relationship of event types to partition the event trigger clusters into event mains and subtypes. The framework employs a latent variable-based neural generation module and a semantic-based clustering module, the former of which obtains event trigger clusters representing the main types of events by jointly projecting the co-occurrence and semantic information of event triggers into a latent space for event type latent variable mining, and the latter of which further divides these event trigger clusters into event subtypes based on semantic information. Finally, experiment results show that, compared with the benchmark model, the ETGen-Clus can improve event type quality scores of 6.23% and 3.11% on the two datasets, respectively.

Finite-time Secondary Frequency Modulation of Microgrid Based on Event Triggering Mechanism

Finite-time Secondary Frequency Modulation of Microgrid Based on Event Triggering Mechanism

Quantized and event-triggered modeling and fault detection for networked fuzzy systems

This article concentrates on the issue of fault detection in the networked system. Quantized output and event-triggered communication scheme are absorbed into the system analysis, and consequently, by designing a new quantizer with event triggering mechanism, a smooth communication environment without undesired system data is guaranteed. And the imperfect information interaction strategy induced asynchronous membership functions between system and fault detection observer are handled via a differential mean value theorem based approach. Combining the methods of Lyapunov stability theory and linear matrix inequality, we obtain the stability criterion of the T-S fuzzy systems closed by the predefined network channel. In the end, simulation examples demonstrate the validity of the fault detection method.

Emergency scheduling based on event triggering and multi-hierarchical planning for space surveillance network

Space Surveillance Network (SSN) task scheduling plays a crucial role in maintaining the catalog of Resident Space Objects (RSO). However, various emergencies, such as RSO maneuvering, collisions, or rocket launch, may disrupt the original scheduled scheme. Therefore, it is essential to rapidly regenerating emergency schemes while minimizing disturbance to the initial scheduling scheme. This paper introduces an Emergency Task Scheduling model, referred to as MM-ETS, which aims to Maximize observation profits and Minimize disturbance. This model incorporates constraints related to observability, tasks, and resources, which are derived from practical applications. Additionally, a Hierarchical Distributed Dynamic Emergency Scheduling algorithm, encompassing Task assignment, Conflict resolution, Resource negotiation, and Center collaboration (HD-TCRC-DES), is proposed. The presented algorithm is activated by emergencies and a Rolling Horizon Strategy (RHS) is employed to break down long-term, large-scale problems into short-term, small-scale problems, thus improving feasibility and emergency response capabilities. At each layer of the proposed algorithm, heuristic rules are utilized to solve the new scheme, which helps allocate the computational load to resource nodes, and quickly adjust the initial scheduling scheme. Experimental scenarios involving 15 ground observation resources and 1000 emergency tasks are constructed, and the simulation results demonstrate that the proposed method can enhance Comprehensive Benefits Indicators (CBI) by approximately 24.65%, 24.7%, 24.91%, and 30.5% compared to the baselines. Consequently, it is suitable for SSN emergency task scheduling.

Adaptive Attitude Control for QUAVs Using Event-Triggered Control and Optimized Quantized Communication with Prescribed Performance

This paper proposes an appointed-time prescribed performance adaptive attitude controller with an event-triggered (ET) mechanism and optimized quantized communication for quadrotor unmanned aerial vehicles (QUAVs) in an uncertain environment. An appointed-time prescribed performance function is first designed to improve the convergence performance of attitude tracking errors considering different convergence time requirements and the convergence rate. In addition, to reduce the communication burden, a switching threshold ET mechanism and an optimized hybrid quantizer are adopted by using the K-means method. The ET mechanism not only involves the fixed threshold but also refers to the change rate of calculated control law flexibly and predictively. The optimized hybrid quantizer creatively classifies signals by their value and maximizes the advantage of each quantization method to reduce excessive quantization errors. It is shown that the proposed control scheme ensures that the closed-loop signals are uniformly ultimately bounded according to the Lyapunov method. Finally, the simulation experiments are implemented on a QUAV to demonstrate the effectiveness of the presented control scheme.

Health literacy, governance and systems leadership contribute to the implementation of the One Health approach: a virtuous circle

One Health is an important approach to addressing health threats and promoting health through interdisciplinary health, policy, legislation and leadership research to achieve better human and animal health and better outcomes for the planet. The Covid-19 pandemic has triggered an urgent awareness of the need to develop innovative integrative solutions to address root causes of such threats to health, which requires collaboration across disciplines and amongst different sectors and communities. We explore how achieving the Quadripartite Organizations’ One Health Joint Plan of Action can be supported by the concepts of ‘One Health literacy’ and ‘One Health governance’ and promote both academic and policy dialogue. We show how One Health literacy and One Health governance influence and reinforce each other, while an interdisciplinary systems leadership approach acts as a catalyst and mechanism for understanding and enacting change. Based on our understanding of how these elements influence the implementation of the One Health approach, we describe a model for considering how external triggering events such as the Covid-19 pandemic may prompt a virtuous circle whereby exposure to and exploration of One Health issues may lead to improved One Health literacy and to better governance. We close with recommendations to international organisations, national governments and to leaders in policy, research and practice to enhance their influence on society, the planetary environment, health and well-being.

Formation fault-tolerant control for multiple UAVs with external disturbances

PurposeThe purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.Design/methodology/approachFirst, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.FindingsIt achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.Originality/valueA formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

Formation mechanisms of persistent extreme precipitation events over the eastern periphery of the Tibetan Plateau: Synoptic conditions, moisture transport and the effect of steep terrain

The eastern periphery of the Tibetan Plateau (EPTP) is prone to frequent and severe Persistent Extreme precipitation (PEP) events in summer. Given the complexity of weather systems and the intricate nature of terrain over this region, the generation and development mechanisms of the PEP in the EPTP remain to be determined. In this study, the formation and persistence mechanisms are further explored from the perspective of the general features of large-scale circulations, moisture source diagnosis and the influence of steep topography by using a thermal-dynamical diagnosis method, a mesoscale numerical simulation and a Lagrangian identification of the main moisture sources. The results show that during PEP events, the corresponding atmospheric circulation systems are characterized by an anomalous Rossby wave train at middle levels, an intensified westerly jet, an eastward extension of the South Asian high and a westward extension of the western Pacific subtropical high, all of which are possible to facilitate the development of potential instability and anticyclonic convergence of water vapor transport. The evaporative moisture sources from the Southeastern Asia (SEA), Bay of Bengal (BOB), and Southern China Sea (SCS) are remarkably enhanced during PEP events, with a contribution of 56.44%, which is 28.5% higher than the climatic mean. The further sensitive experiment indicates that the steep terrain could enhance the continuous transport of positive potential vorticity and the downward propagation of upper-level isentropic potential vorticity disturbances, thereby playing an essential role in the triggering and development of PEP events.

Non-fragile control associated with exponential stability for unmanned surface vessels with the rudder failure and dynamic event-trigger

In this paper, non-fragile control associated with exponential stability for unmanned surface vessels(USVs) with the rudder failure and dynamic event-trigger is presented. To begin with, the event trigger and switch system are introduced, taking into account the distribution characteristics of faults and resource efficiency, and a class of independence-distributed random failure models are built. Furthermore, acknowledging the practical challenges associated with parameter uncertainties, USVs are used as uncertain Nomoto model(NM) and the rudder system is described with uncertain second-order underdamped system(UDS). Then, in the presence of dynamic disturbances, an appropriate non-fragile controller is proposed to demonstrate that the closed-loop system states can be exponentially stabilized through the application of the Lyapunov–Krasovskii functions(LKF) and Linear Matrix Inequalities (LMIs). Lastly, the simulation results confirmed that control design methods proposed are effective.

DE3TC: Detecting Events with Effective Event Type Information and Context

Event Detection (ED) is a crucial information extraction task that aims to identify the event triggers and classify them into predefined event types. However, most existing methods did not perform well when processing events with implicit triggers. And most methods considered ED as a sentence-level task, lacking effective context for event semantics. Moreover, how to maintain good performance under low resource conditions still needs further study. To address these problems, we propose a novel end-to-end ED model called DE3TC, which Detects Events with Effective Event Type Information and Context. We construct an event type-specific Clue to capture the interaction between event type name and trigger words, providing event type information for implicit triggers. For accessing the effective context of event semantics for sentence-level ED, we consider the correlations between types and select similar types’ descriptions as context. With contextualized representation from a contextual encoder, DE3TC learns the event type information for all events including implicit ones. And it performs sentence-level ED efficiently with effective contexts. The empirical results on ACE 2005 and MAVEN datasets show that: (i) DE3TC obtains state-of-the-art performance compared with previous methods. (ii) DE3TC is also excelled under low-resource conditions.

Reinforcement Learning-Based Event-Triggered Active-Battery-Cell-Balancing Control for Electric Vehicle Range Extension

Optimal control techniques such as model predictive control (MPC) have been widely studied and successfully applied across a diverse field of applications. However, the large computational requirements for these methods result in a significant challenge for embedded applications. While event-triggered MPC (eMPC) is one solution that could address this issue by taking advantage of the prediction horizon, one obstacle that arises with this approach is that the event-trigger policy is complex to design to fulfill both throughput and control performance requirements. To address this challenge, this paper proposes to design the event trigger by training a deep Q-network reinforcement learning agent (RLeMPC) to learn the optimal event-trigger policy. This control technique was applied to an active-cell-balancing controller for the range extension of an electric vehicle battery. Simulation results with MPC, eMPC, and RLeMPC control policies are presented along with a discussion of the challenges of implementing RLeMPC.