Events In Process Research Articles

An extended class of multivariate counting processes and its main properties

Abstract In this paper, a new multivariate counting process model (called Multivariate Poisson Generalized Gamma Process) is developed and its main properties are studied. Some basic stochastic properties of the number of events in the new multivariate counting process are initially derived. It is shown that this new multivariate counting process model includes the multivariate generalized Pólya process as a special case. The dependence structure of the multivariate counting process model is discussed. Some results on multivariate stochastic comparisons are also obtained.

Evaluation of Single-Event Effect Current-Carrier Mapping Based on Experimental Data

For single-event radiation damage of power MOSFET devices, this paper aims to establish a statistical analysis method based on external observation (gate/drain current characteristics in irradiation environment) to recognize and evaluate the radiation evolution process and damage mechanism of microscopic physical quantities inside the devices, namely current-carrier (CC) mapping. Firstly, a special data fluctuate–collapse transform analysis method is proposed according to the temporal characteristics of the gate/drain current. Secondly, a carrier dynamic balance ratio based on current data is defined to evaluate the radiation damage degree of the device. TCAD is used to deeply study the relationship between the external current characteristics and the evolution process of internal physical quantities and the damage mechanism. The results show that the current data timing analysis based on fluctuate–collapse transformation can better peer into the evolution process of irradiation events inside the device, and the statistical analysis based on the dynamic balance ratio of carriers can evaluate the severity of irradiation damage to a certain extent.

Joint modelling of longitudinal ordinal and multi-state data.

Joint modeling of longitudinal and survival data is increasingly used in biomedical studies. However, existing joint models are not applicable to model the longitudinal ordinal responses with non-ignorable missing values caused by the occurrence of events in a multi-state process. In this article, we introduce a joint model for longitudinal ordinal measurements and multi-state data. Our proposed joint model consists of two sub-models: a proportional odds sub-model for longitudinal ordinal measurements and a multi-state sub-model with transition-specific proportional hazards for times of transitions between different health states, both linked by shared random effects. The model parameters were estimated employing the maximum likelihood method for a piecewise constant baseline hazard function. The proposed joint model is evaluated in a simulation study and, as an illustration, it is fitted to real data from people with human immunodeficiency virus.

Influence of instructions and expectations in pain perception on pupil diameter and the cognitive assessment of pain

ObjectiveThe purpose of this study was to analyze the influence of pain and no pain expectation on pupil diameter and the cognitive assessment of pain using pupillometry and the visual analog scale (VAS). MethodsThis was an experimental cross-sectional study in which painful stimulation of the muscle masseter was produced by palpation with an algometer in a sample of 30 healthy participants. Before the painful stimulation, pain expectation and no pain expectation situations were induced by employing instructional videos. Cognitive and physiological pain responses were measured by the VAS and by pupillometry. Visual analog scale score was assessed at the end of each experimental condition, and pupillometry measurements were recorded at different moments of the experimental process (events). ResultsThe pain score and larger pupil diameter for all events (except, as expected, for the initial baseline), were statistically significant for the pain expectation condition compared with the no pain expectation. ConclusionWithin the conditions of this study, pain expectation modulated both cognitive assessment of pain (VAS) and pupil diameter. These findings suggest that pupil diameter may be a useful and complementary tool with the VAS for pain assessment. Furthermore, the findings support models postulating that emotion and cognition are important aspects involved in pain perception, which is in line with the recent International Association for the Study of Pain definition of pain.

An empirical analysis of learning and forgetting mechanisms in repeated evacuations: A panel recursive logit approach

Global efforts to address climate change increasingly focus on risk communication to encourage people's mitigation behaviour. This study investigates how risk communication influences evacuation behaviour, through repeated risk communications and stated preference surveys. In modelling the learning process of infrequent events like disasters, it is necessary to consider decision-making under uncertainty about future situations and the tendency to forget acquired information. We propose a recursive model that captures two decision dynamics relevant to infrequent events: proactive decision-making amid dynamically changing conditions and the learning and forgetting processes between events. The former is modelled using the recursive logit model on a time-expanded network, while the latter is represented by dynamic changes in utility and memory decay parameters. The estimation results indicate that risk communication generally promotes earlier evacuation to safer places. However, effective communication strategies and the duration of their effects differ based on individuals' perceived and actual risks.

Shared parameter modeling of longitudinal data allowing for possibly informative visiting process and terminal event.

Joint modeling of longitudinal and time-to-event data, particularly through shared parameter models (SPMs), is a common approach for handling longitudinal marker data with an informative terminal event. A critical but often neglected assumption in this context is that the visiting/observation process is noninformative, depending solely on past marker values and visit times. When this assumption fails, the visiting process becomes informative, resulting potentially to biased SPM estimates. Existing methods generally rely on a conditional independence assumption, positing that the marker model, visiting process, and time-to-event model are independent given shared or correlated random effects. Moreover, they are typically built on an intensity-based visiting process using calendar time. This study introduces a unified approach for jointly modeling a normally distributed marker, the visiting process, and time-to-event data in the form of competing risks. Our model conditions on the history of observed marker values, prior visit times, the marker's random effects, and possibly a frailty term independent of the random effects. While our approach aligns with the shared-parameter framework, it does not presume conditional independence between the processes. Additionally, the visiting process can be defined on either a gap time scale, via proportional hazard models, or a calendar time scale, via proportional intensity models. Through extensive simulation studies, we assess the performance of our proposed methodology. We demonstrate that disregarding an informative visiting process can yield significantly biased marker estimates. However, misspecification of the visiting process can also lead to biased estimates. The gap time formulation exhibits greater robustness compared to the intensity-based model when the visiting process is misspecified. In general, enriching the visiting process with prior visit history enhances performance. We further apply our methodology to real longitudinal data from HIV, where visit frequency varies substantially among individuals.

Dynamics of Vortex Matter in 2D Gapless Superconducting Materials with Impurities.

The recent interest in using ultrafast single-photon detectors in research and commercial applications has garnered significant attention from the scientific community. The dynamic event in the detection process consists of a photon causing local destruction of the order parameter, and then the applied current dissipates heat, bringing the material even more out of the superconducting state and then spiking a voltage peak in a measurement device. We investigated the role of superconducting and thermal parameters of the Generalized Time-Dependent Ginzburg-Landau (GTDGL) theory within the event of the first vortex penetration and the thermal dissipation in superconductors near the critical temperature. Moreover, in the vicinity of the Bogomolny point of the static Ginzburg-Landau theory, where κ ≈ 1/√2 and T → Tc, it has been found severely different vortex profiles. We point out that within the GTDGL theory, the presence of impurities (usual or paramagnetic) influences flux penetration and enhances the dissipation of heat, causing anomalous vortex configurations.

Causal Learning: Monitoring Business Processes Based on Causal Structures.

Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (CaProM), an innovative technique based on causality techniques, applied during the planning phase in business process environments. The technique combines two causal perspectives: anomaly attribution and distribution change attribution. It has three stages: (1) process events are collected and recorded, identifying flow instances; (2) causal learning of process activities, building a directed acyclic graphs (DAGs) represent dependencies among variables; and (3) use of DAGs to monitor the process, detecting anomalies and critical nodes. The technique was validated with a industry dataset from the banking sector, comprising 562 activity flow plans. The study monitored causal structures during the planning and execution stages, and allowed to identify the main factor behind a major deviation from planned values. This work contributes to business process monitoring by introducing a causal approach that enhances both the interpretability and explainability of anomalies. The technique allows to understand which specific variables have caused an atypical scenario, providing a clear view of the causal relationships within processes and ensuring greater accuracy in decision-making. This causal analysis employs cross-sectional data, avoiding the need to average multiple time instances and reducing potential biases, and unlike time series methods, it preserves the relationships among variables.

Signal detection

Acoustic emission (AE) occurring in concrete fracture process is a combination of concrete fracture at local material level and the propagation of induced elastic waves at structural level. Simulation of the fracture-induced AE in concrete needs a model that is able to address the fracture behavior and the wave propagation at the same time. However, to date, no available modelling tools can simulate the whole fracture-induced AE process. Among others, the lattice models have the potential to achieve the simulation of the whole fracture-induced AE process. However, the lattice models are currently restricted to statistical analysis of AE events in fracture process. This paper investigates the feasibility and limitation of lattice model for the simulation of elastic wave propagation process. Several simulation cases are performed considering the propagation of both plane waves and point-source waves. The simulation results, in terms of wave velocity and geometric spreading loss of wave amplitudes, agree well with the analytical solutions. This study represents the first step for the simulation of complete fracture-induced AE waves in concrete.

A lattice modelling framework for fracture-induced acoustic emission wave propagation in concrete

To date, there is no comprehensive approach available that can explicitly model the complete transient waveforms of acoustic emissions (AE) induced by fracture processes in brittle and quasi-brittle materials like concrete. The complexity of AE modelling arises from the intricate coupling between the local discontinuity of material fracturing and the global continuity of elastic wave propagation in solids. Among others, the lattice type models are promising approaches, as they are known to be a matured modelling approach to simulate the fracturing process in concrete-like materials. Nevertheless, like other discrete element methods (DEM), they are currently limited to describing the number and rate of AE events (broken elements) in the fracture process and cannot explicitly model wave generation and propagation. In this study, we propose a lattice modeling framework to simulate the propagation of complete waveforms of fracture-induced AE signals in concrete. A proportional-integral-derivative (PID) control algorithm is incorporated in an explicit time integration procedure to reduce dynamic noise from spurious oscillations. Additionally, a Rayleigh damping-based calculation method and corresponding calibration procedure are proposed to model the attenuation of AE signals due to material damping. Using the developed approach, we systematically investigate the feasibility of lattice models for elastic wave propagation simulation, the dependence of lattice mesh sizes and the choice of numerical damping parameters. These results lead to a systematic framework which can be employed in simulating wave propagation with attenuation using DEM models in general including lattice models.

Weaving Our Kuwentos (Stories) toward Ginhawa (Aliveness): Pilipinx American Social Work MotherScholars Enacting Praxes of Survival and Thrivance in the Academy

Five Pilipina American (PA) social work MotherScholars, from a doctoral student to an interim dean, used kuwentuhan (Pilipinx methodology) to amplify their survivance and thrivance despite attempted exclusion, reduction, and distortion as Pilipinos by coloniality/modernity. Grounded in decolonial feminism (the view that oppressions such as sexism and racism co-constitute coloniality and that unsettling oppressions disrupts hegemony) and Pinayism (an integrated framework revaluing the labor, intellect, and nurturance of mothering through a cultural lens), the authors work coalitionally across their PA diversity to re-center ginhawa (aliveness or sense of ease and wellness). Together, they embarked on an iterative self-study process of data generation and analysis that included presenting, recording, and transcribing two panel presentations at a premier social work conference, writing reflections and hay(na)ku poems about their experiences and processes, reading and rereading the data, and meeting and discussing the data, their process, and past and current events pertinent to the content. The stories highlight how the authors are living and enlivening decoloniality, and that, in so doing, they continue a lineage of those who have resisted coloniality/modernity and promoted thrivance. Collectively, these kuwentos (stories), reflections, hay(na)ku, and their weaving together, are memory, resistance, counter-storytelling, and healing.

More or Less Likeable: The Role of Behavior-Independent Events in the Disposition Formation Process

ABSTRACT Affective disposition theory (ADT) and its extensions posit that character liking/disliking is based on viewer perceptions of character behaviors or the activation of character schemas that imply moral/immoral behaviors. This paper systematically examines whether events that happen to a character independent of their behavior also contribute to the development of character liking. Based on the modified affective disposition model (MADM), two studies were conducted where participants read a story introduction that manipulated whether a positive or negative event (described as egoistic need satisfaction/thwarting by the MADM) had befallen a character. In Study 1, the character was liked more when a negative economic event had befallen them and liked less when a positive economic event had befallen them as compared to a control condition. In Study 2, these effects were replicated. However, in Study 2 we also examined whether health-related events would elicit similar effects. Participants liked the character more regardless of whether a positive or negative health event befell her. These findings suggest unique contextual effects associated with which specific egoistic intuitions are satisfied or thwarted. As such they provide novel directions for research designed to test and specify the predictions of the MADM.

Timed Genetic Process Mining for Robust Tracking of Processes under Incomplete Event Log Conditions

In process mining, an event log is a structured collection of recorded events that describes the execution of processes within an organization. The completeness of event logs is crucial for ensuring accurate and reliable process models. Incomplete event logs, which can result from system errors, manual data entry mistakes, or irregular operational patterns, undermine the integrity of these models. Addressing this issue is essential for constructing accurate models. This research aims to enhance process model performance and robustness by transforming incomplete event logs into complete ones using a process discovery algorithm. Genetic process mining, a type of process discovery algorithm, is chosen for its ability to evaluate multiple candidate solutions concurrently, effectively recovering missing events and improving log completeness. However, the original form of the genetic process mining algorithm is not optimized for handling incomplete logs, which can result in incorrect models being discovered. To address this limitation, this research proposes a modified approach that incorporates timing information to better manage incomplete logs. By leveraging timing data, the algorithm can infer missing events, leading to process tracking and reconstruction which is more accurate. Experimental results validate the effectiveness of the modified algorithm, showing higher fitness and precision scores, improved process model comparisons, and a good level of coverage without errors. Additionally, several advanced metrics for conformance checking are presented to further validate the process models and event logs discovered by both algorithms.

Summer heat wave in 2022 led to rapid warming of permafrost in the central Qinghai-Tibet Plateau

Extreme events with increasing frequency and intensity are significantly affecting the permafrost environment. Analysis using the ERA5-Land reanalysis data revealed that the permafrost region of the central Qinghai-Tibet Plateau (QTP) experienced the summer heat wave in 2022. Four active layer sites experienced maximum active layer thicknesses (ALT) in 2022 (mean: 207.7 cm), which was 20% higher than the mean ALT during 2000–2021 (mean: 175.9 cm). The mean annual ground temperature (MAGT) observed in 2022 was also the highest, exceeding the average of the previous years by 10%. The contribution fraction of heat wave to the seasonal thaw depth of active layer was quantified using Stefan model with ranging from 6.6% to 13.6%, and the maximum contribution fraction occurs in 2022. These findings are helpful to better understand the impact processes of extreme events on the active layer and permafrost.

Hydramethylnon induces mitochondria-mediated apoptosis in BEAS-2B human bronchial epithelial cells

Typically used household chemicals comprise numerous compounds. Determining mixture toxicity, as observed when using household chemicals containing multiple substances, is of considerable importance from a regulatory perspective. Upon examining the toxic effects of household chemical mixtures, we observed that hydramethylnon combined with tetramethrin resulted in synergistic toxicity. To determine the unknown toxicity mechanism of hydramethylnon, which carries the risk of inhalation exposure when using household chemicals, we conducted a further investigation using BEAS-2B cells, a human bronchial epithelial cell line. Hydramethylnon-induced cytotoxicity was determined following 24 and 48 h of exposure using the water-soluble tetrazolium 1 and lactate dehydrogenase assays. To elucidate the toxicity mechanism, we utilized flow cytometry and measured the levels of apoptosis-related proteins and caspase activities. Given that hydramethylnon, as an insecticide, disrupts the mitochondrial electron transfer chain, we analyzed the relevant mechanisms, including mitochondrial superoxide levels as well as the mitochondrial membrane potential (MMP). Hydramethylnon dose-dependently induced BEAS-2B cell apoptosis via the intrinsic pathway. Furthermore, it significantly increased mitochondrial superoxide levels and disrupted the MMP. Pre-treatment with a caspase inhibitor (Z-DEVD-FMK) confirmed that hydramethylnon induced caspase-dependent apoptosis. Apoptosis, a key event in the toxicological process of chemicals, can lead to lung diseases, including fibrosis and cancer. The results of the present study suggest a mechanism of toxicity of hydramethrylnon, an organofluorine biocide whose toxicity has been little studied, to the lung epithelium. Considering the potential risks associated with inhalation exposure, these results highlight the need for careful management and regulation of hydramethylnon.

KF-PEV: a causal Kalman filter-based particle event velocimetry

Event-based pixel sensors asynchronously report changes in log-intensity in microsecond-order resolution. Its exceptional speed, cost effectiveness, and sparse event stream make it an attractive imaging modality for particle tracking velocimetry. In this work, we propose a causal Kalman filter-based particle event velocimetry (KF-PEV). Using the Kalman filter model to track the events generated by the particles seeded in the flow medium, KF-PEV yields the linear least squares estimate of the particle track velocities corresponding to the flow vector field. KF-PEV processes events in a computationally efficient and streaming manner (i.e., causal and iteratively updating). Our simulation-based benchmarking study with synthetic particle event data confirms that the proposed KF-PEV outperforms the conventional frame-based particle image/tracking velocimetry as well as the state-of-the-art event-based particle velocimetry methods. In a real-world water tunnel event-based sensor data experiment performed on what we believe to be the widest field view ever reported, KF-PEV accurately predicted the expected flow field of the SD7003 wing, including details such as the lower velocity in the wake and the flow separation around the underside of an angled wing.

The posterior intralaminar thalamic nucleus promotes nose-to-nose contacts leading to prosocial reception in the sequence of mouse social interaction.

Efficient social interaction is essential for an adaptive life and consists of sequential processes of multisensory events with social counterparts. Social touch/contact is a unique component that promotes a sequence of social behaviours initiated by detection and approach to assess a social stimulus and subsequent touch/contact interaction to form prosocial relationships. We hypothesized that the thalamic sensory relay circuit from the posterior intralaminar nucleus of the thalamus (pIL) to the paraventricular nucleus of the hypothalamus (PVN) and the medial amygdala (MeA) plays a key role in the social contact-mediated sequence of events. We found that neurons in the pIL along with the PVN and MeA were activated by social encounters and that pIL activity was more abundant in a direct physical encounter, whereas MeA activity was dominant in an indirect through grid encounter. Chemogenetic inhibition of pIL neurons selectively decreased the investigatory approach and sniffing of a same-sex, but not an opposite-sex, stimulus mouse in an indirect encounter situation and decreased the facial/snout contact ratio in a direct encounter setting. Furthermore, chemogenetic pIL inhibition had no impact on anxiety-like behaviours or body coordinative motor behaviours, but it impaired whisker-related and plantar touch tactile sense. We propose that the pIL circuit can relay social tactile sensations and mediate the sequence of nonsexual prosocial interactions through an investigatory approach to tactile contact and thus plays a significant role in establishing prosocial relationships in mouse models.

Spatio-temporal evolution law of anisotropic shear damage on rock mass joint surface affected by joint morphology

The spatial–temporal evolution law of shear damage on the joint surface is closely related to its morphological characteristics. Understanding the heterogeneous failure behavior of the joint surface is essential for revealing the controlling role of the joint morphology. The diorite from the Guanshan Tunnel is investigated through anisotropic direct shear tests in this study. Based on shear damage quantification and acoustic emission (AE) localization techniques, the heterogeneous damage evolution law of joint surfaces under different joint compression strength (JCS), normal stress (σn), and morphology parameter (M) is studied. The results indicate that the joint surface morphology, shear damage, and AE parameters exhibit similar anisotropic characteristics in different shear directions. An increase in the morphology parameter leads to an increase in shear failure, with shear fracture energy and the number of fracture events showing an increasing trend. Meanwhile, the occurrence of a large number of fracture points shifts from the pre-peak nonlinear period to the post-peak period. However, the evolution process of fracture events exhibits certain similarities, though the morphology parameters are different in two opposite shear directions. Additionally, shear damage volume and fracture energy demonstrate consistency in spatial distribution and quantitative characteristics, while the linear scale factor between cumulative shear fracture energy and damage volume decreases with increasing joint surface strength. These findings assist in a better understanding of the anti-sliding property of joint surfaces and provide crucial clues for further exploration of joint surface failure mechanisms.

A latent profile analysis and network analysis of anxiety and depression symptoms in Chinese widowed elderly

BackgroundWidowhood, as a traumatic event in the aging process, may lead to adverse psychological consequences such as anxiety and depression. However, the heterogeneity of anxiety and depression comorbidity patterns in widowed elderly and the interrelationships between symptoms have not been adequately studied. Method10,239 elderly aged 65 years and older were screened from the Chinese Longitudinal Healthy Longevity Survey (CLHLS 2017–2018), to assess depression and anxiety using the Center for Epidemiologic Studies Depression Scale (CESD) and the 7-item Generalized Anxiety Disorder Questionnaire (GAD). The subgroups of widowed elderly with similar patterns of symptoms were identified by latent profile analysis (LPA). The structure of anxiety-depressive comorbidity network was characterized using “bridge expected influence” as centrality indices. Network stability was tested using a case drop bootstrap program. A network comparison test (NCT) was performed to examine the differences in network characteristics across LPA subgroups. ResultLPA identified dichotomous profiles: low comorbid (n = 4457) and high comorbid (n = 692). NCT revealed a significant difference in the global strength between networks (S = 0.631, p < 0.001). GAD1 (Nervousness or anxiety) is the common bridging symptom for both networks, while the bridging symptom for the high comorbidity network also includes GAD3 (Generalized worry). LimitationsCross-sectional methods are unable to verify causal relationships, and further randomized controlled trials are warranted. ConclusionAnxiety-depressive pattern in Chinese widowed elderly can be categorized into a low comorbid or a high comorbid group. GAD3 (Generalized worry) can be used as the core intervention target during intervention.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases.

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although,there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.