Sequence Of Events Research Articles

Time-dependent sequential association rule-based survival analysis: A healthcare application

The analysis of event sequences with temporal dependencies holds substantial importance across various domains, including healthcare. This study introduces a novel approach that combines sequential rule mining and survival analysis to uncover significant associations and temporal patterns within event sequences. By integrating these techniques, we address the limitations linked to the loss of temporal information. The methodology extends traditional sequential rule mining by introducing time-dependent confidence functions, providing a comprehensive understanding of relationships between antecedent and consequent events. The incorporation of the Kaplan-Meier estimator of survival analysis enables the calculation of temporal distributions between events, resulting in time-dependent confidence functions. These confidence functions illuminate the probability of specific event occurrences considering temporal contexts. To present the application of the method, we demonstrated the usage within the healthcare domain. Analyzing the ICD-10 codes and the laboratory events, we successfully identified relevant sequential rules and their time-dependent confidence functions. This empirical validation underscores the potential of methodology to uncover clinically significant associations within intricate medical data.•The study presents a unique methodology that integrates sequential rule mining and survival analysis.•The methodology extends traditional sequential rule mining by introducing time-dependent confidence functions.•The application of the method is demonstrated within the healthcare domain.

Application of a variational autoencoder for clustering and analyzing in situ articular cartilage cellular response to mechanical stimuli.

In various biological systems, analyzing how cell behaviors are coordinated over time would enable a deeper understanding of tissue-scale response to physiologic or superphysiologic stimuli. Such data is necessary for establishing both normal tissue function and the sequence of events after injury that lead to chronic disease. However, collecting and analyzing these large datasets presents a challenge-such systems are time-consuming to process, and the overwhelming scale of data makes it difficult to parse overall behaviors. This problem calls for an analysis technique that can quickly provide an overview of the groups present in the entire system and also produce meaningful categorization of cell behaviors. Here, we demonstrate the application of an unsupervised method-the Variational Autoencoder (VAE)-to learn the features of cells in cartilage tissue after impact-induced injury and identify meaningful clusters of chondrocyte behavior. This technique quickly generated new insights into the spatial distribution of specific cell behavior phenotypes and connected specific peracute calcium signaling timeseries with long term cellular outcomes, demonstrating the value of the VAE technique.

Motorcycle injuries: a systematic review for forensic evaluation.

The intricate interplay of exposure and speed leave motorcyclists vulnerable, leading to high mortality rates. During the collision, the driver and the passenger are usually projected away from the motorcycle, with variable trajectories or final positions. Injuries resulting from the crash can exhibit distinct and specific characteristics depending on the circumstances of the occurrence.The aim of this study is to provide a systematic review of the literature on injuries sustained by motorcyclists involved in road accidents describing and analyzing elements that are useful for forensic assessment.The literature search was performed using PubMed, Scopus and Web of Science from January 1970 to June 2023. Eligible studies have investigated issues of interest to forensic medicine about during traffic accidents involving motorcycle. A total of 142 studies met the inclusion criteria and were classified and analyzed based on the anatomical regions of the body affected (head, neck, thoraco-abdominal, pelvis, and limb injuries). Moreover, also the strategies for preventing lesions and assessing injuries in the reconstruction of motorcycle accidents were examined and discussed.This review highlights that, beyond injuries commonly associated with motorcycle accidents, such as head injuries, there are also unique lesions linked to the specific dynamics of accidents. These include factors like the seating position of the passenger or impact with the helmet or motorbike components. The forensic assessment of injury distribution could serve as support in reconstructing the sequence of events leading to the crash and defining the cause of death in trauma fatalities.

Measuring temporal bias in sequential numerosity comparison.

While several methods have been proposed to assess the influence of continuous visual cues in parallel numerosity estimation, the impact of temporal magnitudes on sequential numerosity judgments has been largely ignored. To overcome this issue, we extend a recently proposed framework that makes it possible to separate the contribution of numerical and non-numerical information in numerosity comparison by introducing a novel stimulus space designed for sequential tasks. Our method systematically varies the temporal magnitudes embedded into event sequences through the orthogonal manipulation of numerosity and two latent factors, which we designate as "duration" and "temporal spacing". This allows us to measure the contribution of finer-grained temporal features on numerosity judgments in several sensory modalities. We validate the proposed method on two different experiments in both visual and auditory modalities: results show that adult participants discriminated sequences primarily by relying on numerosity, with similar acuity in the visual and auditory modality. However, participants were similarly influenced by non-numerical cues, such as the total duration of the stimuli, suggesting that temporal cues can significantly bias numerical processing. Our findings highlight the need to carefully consider the continuous properties of numerical stimuli in a sequential mode of presentation as well, with particular relevance in multimodal and cross-modal investigations. We provide the complete code for creating sequential stimuli and analyzing participants' responses.

Multimodular Wide-Band Capturing Nanohybrids: Role of Carbon Nanotubes in Slowing Charge Recombination in Supramolecular C60-BisstyrylBODIPY-(Zinc Porphyrin)2 Donor-Acceptor Molecular Cleft.

The importance of diameter-sorted single-wall carbon nanotubes (SWCNTs) noncovalently bound to a donor-acceptor molecular cleft, 1, in prolonging the lifetime of charge-separated states is successfully demonstrated. For this, using a multistep synthetic procedure, a wide-band capturing, multimodular, C60-bisstyrylBODIPY-(zinc porphyrin)2, molecular cleft 1, was newly synthesized and shown to bind diameter-sorted SWCNTs. The molecular cleft and its supramolecular assemblies were characterized by a suite of physicochemical techniques. Free-energy calculations suggested that both the (6,5) and (7,6) SWCNTs bound to 1 act as hole acceptors during the photoinduced sequential electron transfer events. Consequently, selective excitation of 1 in 1:SWCNT hybrids revealed a two-step electron transfer, leading to the formation of charge-separated states. Due to the distant separation of the cation and anion radical species within the supramolecules, improved lifetimes of the charge-separated states could be achieved. The present supramolecular strategy of improving charge separation involving SWCNTs and donor-acceptor molecular clefts highlights the potential application of these hybrid materials for various light energy harvesting and optoelectronic applications.

Simulating multi-hazard event sets for life cycle consequence analysis

Abstract. In the context of natural hazard risk quantification and modeling of hazard interactions, some literature separates “Level I” (or occurrence) interactions from “Level II” (or consequence) interactions. The Level I interactions occur inherently due to the nature of the hazards, independently of the presence of physical assets. In such cases, one hazard event triggers or modifies the occurrence of another (e.g., flooding due to heavy rain, liquefaction and landslides triggered by an earthquake), thus creating a dependency between the features characterizing such hazard events. They differ from Level II interactions, which instead occur through impacts/consequences on physical assets/components and systems (e.g., accumulation of physical damage or social impacts due to earthquake sequences, landslides due to the earthquake-induced collapse of a retaining structure). Multi-hazard life cycle consequence (LCCon) analysis aims to quantify the consequences (e.g., repair costs, downtime, casualty rates) throughout a system’s service life and should account for both Level I and II interactions. The available literature generally considers Level I interactions – the focus of this study – mainly defining relevant taxonomies, often qualitatively, without providing a computational framework to simulate a sequence of hazard events incorporating the identified interrelations among them. This paper addresses this gap, proposing modeling approaches associated with different types of Level I interactions. It describes a simulation-based method for generating multi-hazard event sets (i.e., a sequence of hazard events and associated features throughout the system’s life cycle) based on the theory of competing Poisson processes. The proposed approach incorporates the different types of interactions in a sequential Monte Carlo sampling method. The method outputs multi-hazard event sets that can be integrated into LCCon frameworks to quantify interacting hazard consequences. An application incorporating several hazard interactions is presented to illustrate the potential of the proposed method.

XAI Human-Machine collaboration applied to network security

Cyber attacking is easier than cyber defending—attackers only need to find one breach, while the defenders must successfully repel all attacks. This research demonstrates how cyber defenders can increase their capabilities by joining forces with eXplainable-AI (XAI) utilizing interactive human-machine collaboration. With a global shortfall of cyber defenders there is a need to amplify their skills using AI. Cyber asymmetries make propositional machine learning techniques impractical. Human reasoning and skill is a key ingredient in defense and must be embedded in the AI framework. For Human-Machine collaboration to work requires that the AI is an ultra-strong machine learner and can explain its models. Unlike Deep Learning, Inductive Logic Programming can communicate what it learns to a human. An empirical study was undertaken using six months of eavesdropped network traffic from an organization generating up-to 562K network events daily. Easier-to-defend devices were identified using a form of the Good-Turing Frequency estimator which is a promising form of volatility measure. A behavioral cloning grammar in explicit symbolic form was then produced from a single device's network activity using the compression algorithm SEQUITUR. A novel visualization was generated to allow defenders to identify network sequences they wish to explain. Interactive Inductive Logic Programming (the XAI) is supplied the network traffic meta data, sophisticated pre-existing cyber security background knowledge, and one recurring sequence of events from a single device to explain. A co-inductive process between the human cyber defender and the XAI where the human is able to understand, then refute and shape the XAI's developing model, to produce a model that conforms with the data as well as the original device designers programming. The acceptable model is in a form that can be deployed as an ongoing active cyber defense.

Activating Iterative Revolutions of the Cancer-Immunity Cycle in Hypoxic Tumors with a Smart Nano-Regulator.

The activation of sequential events in the cancer-immunity cycle (CIC) is crucial for achieving effective anti-tumor immunity. However, formidable challenges, such as innate and adaptive immune resistance, along with the off-target adverse effects of non-selective immunomodulators, persist. In this study, a tumor-selective nano-regulator named PNBJQ has been presented, focusing on targeting two non-redundant immune nodes: inducing immunogenic cancer cell death and abrogating immune resistance to fully activate endogenous tumor immunity. PNBJQ is obtained by encapsulating the immunomodulating agent JQ1 within a self-assembling system formed by linking a Type-I photosensitizer to polyethylene glycol through a hypoxia-sensitive azo bond. Benefiting from the Type-I photosensitive mechanism, PNBJQ triggers the immunogenic cell death of hypoxic tumors under NIR light irradiation. This process resolves innate immune resistance by stimulating sufficient cytotoxic T-lymphocytes. Simultaneously, PNBJQ smartly responds to the hypoxic tumor microenvironment for precise drug delivery, adeptly addressing adaptive immune resistance by using JQ1 to down-regulate PD-L1 and sustaining the response of cytotoxic T lymphocytes. The activatable synergic photoimmunotherapy promotes an immune-promoting tumor microenvironment by activating an iterative revolution of the CIC, which remarkably eradicates established hypoxic tumors and suppresses distal lesions under low light dose irradiation. This article is protected by copyright. All rights reserved.

Automatically Temporal Labeled Data Generation Using Positional Lexicon Expansion for Focus Time Estimation of News Articles

Many facts change over time, which is a fundamental aspect of our physical environment. In the case of pandemic articles, the user is not interested in the creation date of the document but in the facts and the cause of the last pandemic. Fake news can be better combated by having a document with a temporal focus. Currently, neither the sequence of events nor the temporal focus is considered when obtaining news documents. Despite the limited number of temporal aspects in the available datasets, it is difficult to test and evaluate the temporal conclusions of the model. The goal of this work is to develop a temporal focus news article retrieval model based on co-training to advance research in semi-supervised learning. A mapping of the dataset is performed using (1) the evolving focus time of news articles and (2) the semi-supervised method based on coincidence contexts for learning low-dimensional continuous vectors for learning neural contrast embedding models generating focus time-based query in sequential news articles to facilitate temporal understanding by learning low-dimensional continuous vectors. A diverse dataset of news articles is used to evaluate the effectiveness of the proposed method. With semi-supervised learning and lexicon expansion, the result of the developed model can achieve 89%. The method performed better than previous baselines and traditional machine learning models with improvements of 12.65% and 4.7%, respectively.

Therapeutic Potential of Nanocarrier Mediated Delivery of Peptides for Wound Healing: Current Status, Challenges and Future Prospective.

Wound healing presents a complex physiological process that involves a sequence of events orchestrated by various cellular and molecular mechanisms. In recent years, there has been growing interest in leveraging nanomaterials and peptides to enhance wound healing outcomes. Nanocarriers offer unique properties such as high surface area-to-volume ratio, tunable physicochemical characteristics, and the ability to deliver therapeutic agents in a controlled manner. Similarly, peptides, with their diverse biological activities and low immunogenicity, hold great promise as therapeutics in wound healing applications. In this review, authors explore the potential of peptides as bioactive components in wound healing formulations, focusing on their antimicrobial, anti-inflammatory, and pro-regenerative properties. Despite the significant progress made in this field, several challenges remain, including the need for standardized characterization methods, optimization of biocompatibility and safety profiles, and translation from bench to bedside. Furthermore, developing multifunctional nanomaterial-peptide hybrid systems represents promising avenues for future research. Overall, the integration of nanomaterials made up of natural or synthetic polymers with peptide-based formulations holds tremendous therapeutic potential in advancing the field of wound healing and improving clinical outcomes for patients with acute and chronic wounds.

Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event Sequences

Frequent sequential pattern mining is a valuable technique for capturing the relative arrangement of learning events, but current algorithms often return excessive learning event patterns, many of which may be noise or redundant. These issues exacerbate researchers’ burden when interpreting the patterns to derive actionable insights into learning processes. This study proposed permutation tests for identifying sequential patterns whose occurrences are statistically significantly greater than the chance value and different from their superpatterns. Simulations demonstrated that the test for detecting sound patterns had a low false discovery rate and high power, while the test for detecting nonredundant patterns also showed a high accuracy. Empirical data analyses found that the patterns detected in training data were generalizable to test data.

Incremental Concolic Testing of Register-Transfer Level Designs

Concolic testing is a scalable solution for automated generation of directed tests for validation of hardware designs. Unfortunately, concolic testing fails to cover complex corner cases such as hard-to-activate branches. In this article, we propose an incremental concolic testing technique to cover hard-to-activate branches in register-transfer level (RTL) models. We show that a complex branch condition can be viewed as a sequence of easy-to-activate events. We map the branch coverage problem to the coverage of a sequence of events. We propose an efficient algorithm to cover the sequence of events using concolic testing. Specifically, the test generated to activate the current event is used as the starting point to activate the next event in the sequence. Experimental results demonstrate that our approach can be used to generate directed tests to cover complex corner cases in RTL models while state-of-the-art methods fail to activate them.

Dynamic rebalancing optimization for bike-sharing systems: A modeling framework and empirical comparison

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternative. As a remedy to unbalanced stations, operators typically rebalance bikes by trucks. The resulting dynamic planning has received significant attention from the Operations Research community. Due to its modeling flexibility, mixed-integer programming remains a popular choice. However, the complex planning problem requires significant simplifications to obtain a computationally tractable model. As a result, existing models have used a large variety of modeling assumptions and techniques regarding decision variables and constraints. Unfortunately, the impact of such assumptions on the solutions’ performance in practice remains generally unexplored.In this paper, we first systematically survey the literature on rebalancing problems and their modeling assumptions. We then propose a general mixed-integer programming model for multi-period rebalancing problems that can be easily adapted to different assumptions, including trip modeling, time discretization, trip distribution, and event sequences. We develop an instance generator to synthesize realistic station networks and customer trips, as well as a realistic fine-grained simulator to evaluate the operational performance of rebalancing strategies. Finally, extensive numerical experiments are carried out, both on the synthetic and real-world data, to analyze the effectiveness of various modeling assumptions and techniques. Based on our results, we identify the assumptions that empirically provide the most effective rebalancing strategies in practice. Specifically, a set of specific trip distribution constraints and event sequences ignored in the previous literature seem to provide particularly good results.

So Let's Design a Peptide . . . A Physician's Approach.

This paper outlines a process undertaken by a physician to design a peptide aimed at impacting the extracellular matrix. From a position of very little expertise, a new peptide was designed with amino acid constituents based on the structural proteins collagen and elastin. Sequencing was also considered, given the periodic repetition observed in these proteins, and a peptide with reasonable molecular weight and physical characteristics was designed using available software. The sequence of events concerning intellectual property, functionality investigation, and eventual use of the peptide in new formulations is detailed. This may be of interest to physicians who consider this exercise out of the scope of the usual practice. J Drugs Dermatol. 2024;23(5):347-352.    doi:10.36849/JDD.7921.

Supernova Archaeology with X-Ray Binary Winds: The Case of GRO J1655−40

Supernovae are responsible for the elemental enrichment of the galaxy, and some are postulated to leave behind a black hole. In a stellar binary system, the supernova pollutes its companion, and the black hole can accrete back its own debris and emit X-rays. In this sequence of events, which is only poorly understood, winds are ejected and observed through X-ray absorption lines. Measuring abundances of elements in the wind can lead to inferences about the historical explosion and possibly identify the long-gone progenitor of the compact object. Here, we reanalyze the uniquely rich X-ray spectrum of the 2005 outburst of GRO J1655−40. We reconstruct the absorption measure distribution (AMD) of the wind and find that it increases sharply with ionization from H-like O up to H-like Ca and then flattens out. The AMD is then used to measure the relative abundances of 18 different elements. The present abundances are in partial agreement with a previous work with discrepancies mostly for low-Z elements. The overabundance of odd-Z elements hints at a high-metallicity, high-mass (≃25 M ⊙) progenitor. Interestingly, the abundances are different from those measured in the companion atmosphere, indicating that the wind entrains lingering ambient supernova debris. This can be expected since the current total stellar mass of the binary (<10 M ⊙) is much less than the progenitor mass.

Dynamic characteristics of droplets impacting an inclined wall based on a front-tracking method

In the current study, the dynamic characteristics of droplets impacting a hydrophilic/hydrophobic inclined wall are investigated using the front-tracking method. The morphological and kinematic features of the droplets are examined across various contact angles (θ), low Weber numbers (We≤10), and inclination angles (α). A sequence of events, including deposition, spreading, contraction, rebound, and subsequent re-spreading, is observed when a droplet impacts a hydrophobic wall, according to the results. The droplet exhibits both a small dimensionless spreading area (A*) and significant fluctuation, along with a noticeable slip on the wall. As We increases, the spread and rebound effects become more pronounced, and the slipping velocity also increases. As α increases, the slipping velocity becomes greater, and both A* and the dimensionless tangential spreading diameter (β) decrease. When a droplet impacts a hydrophilic wall, continuous deposition, spreading, and a subtle slip with inconspicuous rebound are observed. As We increases, A* expands at an accelerating rate. Furthermore, the wetting effect of hydrophilic surfaces on droplets cannot be overlooked. Consequently, it also shows that as α increases, there is a larger diffusion range along the wall with a longer diffusion film and a larger β. Such behaviors stand in contrast to a droplet impacting a hydrophobic wall.

A cross-attention augmented model for event-triggered context-aware story generation

Despite recent advancements, existing story generation systems continue to encounter difficulties in effectively incorporating contextual and event features, which greatly influence the quality of generated narratives. To tackle these challenges, we introduce a novel neural generation model, EtriCA, that enhances the relevance and coherence of generated stories by employing a cross-attention mechanism to map context features onto event sequences through residual mapping. This feature capturing mechanism enables our model to exploit logical relationships between events more effectively during the story generation process. To further enhance our proposed model, we employ a post-training framework for knowledge enhancement (KeEtriCA) on a large-scale book corpus. This allows EtriCA to adapt to a wider range of data samples. This results in approximately 5% improvement in automatic metrics and over 10% improvement in human evaluation. We conduct extensive experiments, including comparisons with state-of-the-art (SOTA) baseline models, to evaluate the performance of our framework on story generation. The experimental results, encompassing both automated metrics and human assessments, demonstrate the superiority of our model over existing state-of-the-art baselines. These results underscore the effectiveness of our model in leveraging context and event features to improve the quality of generated narratives.

Lunar Farside South Pole‐Aitken Basin Interior: Evidence for More Extensive Central Cryptomaria in the South Pole‐Aitken Compositional Anomaly (SPACA)

Lunar Farside South Pole‐Aitken Basin Interior: Evidence for More Extensive Central Cryptomaria in the South Pole‐Aitken Compositional Anomaly (SPACA)

A scale-free model of acute and ventilator-induced lung injury: a network theory approach inspired by seismology.

Acute respiratory distress syndrome (ARDS) presents a significant clinical challenge, with ventilator-induced lung injury (VILI) being a critical complication arising from life-saving mechanical ventilation. Understanding the spatial and temporal dynamics of VILI can inform therapeutic strategies to mitigate lung damage and improve outcomes. Histological sections from initially healthy mice and pulmonary lavage-injured mice subjected to a second hit of VILI were segmented with Ilastik to define regions of lung injury. A scale-free network approach was applied to assess the correlation between injury regions, with regions of injury represented as 'nodes' in the network and 'edges' quantifying the degree of correlation between nodes. A simulated time series analysis was conducted to emulate the temporal sequence of injury events. Automated segmentation identified different lung regions in good agreement with manual scoring, achieving a sensitivity of 78% and a specificity of 85% across 'injury' pixels. Overall accuracy across 'injury', 'air', and 'other' pixels was 81%. The size of injured regions followed a power-law distribution, suggesting a 'rich-get-richer' phenomenon in the distribution of lung injury. Network analysis revealed a scale-free distribution of injury correlations, highlighting hubs of injury that could serve as focal points for therapeutic intervention. Simulated time series analysis further supported the concept of secondary injury events following an initial insult, with patterns resembling those observed in seismological studies of aftershocks. The size distribution of injured regions underscores the spatially heterogeneous nature of acute and ventilator-induced lung injury. The application of network theory demonstrates the emergence of injury 'hubs' that are consistent with a 'rich-get-richer' dynamic. Simulated time series analysis demonstrates that the progression of injury events in the lung could follow spatiotemporal patterns similar to the progression of aftershocks in seismology, providing new insights into the mechanisms of injury distribution and propagation. Both phenomena suggest a potential for interventions targeting these injury 'hubs' to reduce the impact of VILI in ARDS management.

X-Rays from a Central “Exhaust Vent” of the Galactic Center Chimney

Using deep archival observations from the Chandra X-ray Observatory, we present an analysis of linear X-ray-emitting features located within the southern portion of the Galactic center chimney and oriented orthogonal to the Galactic plane, centered at coordinates l = 0.°08, b = −1.°42. The surface brightness and hardness ratio patterns are suggestive of a cylindrical morphology, which may have been produced by a plasma outflow channel extending from the Galactic center. Our fits of the feature’s spectra favor a complex two-component model consisting of thermal and recombining plasma components, possibly a sign of shock compression or heating of the interstellar medium by outflowing material. Assuming a recombining plasma scenario, we further estimate the cooling timescale of this plasma to be on the order of a few hundred to thousands of years, leading us to speculate that a sequence of accretion events onto the Galactic black hole may be a plausible quasi-continuous energy source to sustain the observed morphology.