Clustering Of Events Research Articles

Ore body domaining by clustering of multiple-point data events; a case study from the Dalli porphyry copper-gold deposit, central Iran

Ore body domaining by clustering of multiple-point data events; a case study from the Dalli porphyry copper-gold deposit, central Iran

Post-injection syndrome and olanzapine long-acting injection in patients with schizophrenia: A case series

Pamoate salt of atypical antipsychotic Olanzapine in long-acting injection depot formulation (OLAI) is often used in patients with schizophrenia having poor compliance. This depots formulation having a cluster of adverse events characterized by post-injection delirium/sedation syndrome (PDSS). Here, we present three cases of different age group, different gender suffering from Schizophrenia of varied duration of illness who developed PDSS after receiving OLAI, how they were identified and promptly managed with early intervention. PDSS noted in approximately 0.07% of the injections, in 1.3% of patients. Symptoms such as sedation, delirium, extrapyramidal symptoms, ataxia, autonomic dysfunction, aggression, and speech impairment were reported, onset was immediate to 3–5 h post-injection, patients recovered within 2–72 h. No such risk factors could be identified in any of the cases. PDSS following OLAI occurs when there is accidental intravascular release of high dose of olanzapine. The importance of post-injection syndrome in the above cases lies in the fact that following administration of OLAI a 3–4-h observation period in a health-care facility in the presence of an appropriate qualified personnel is of utmost importance. Furthermore, one should rule out other causes of acute sedation in such patients before establishing the diagnosis of PDSS. There is no specific antidote for olanzapine and almost all such patients improve with conservative and supportive treatment.

Rapid astrocyte-dependent facilitation amplifies multi-vesicular release in hippocampal synapses

Synaptic facilitation is a major form of short-term plasticity typically driven by an increase in residual presynaptic calcium. Using near-total internal reflection fluorescence (near-TIRF) imaging of single vesicle release in cultured hippocampal synapses, we demonstrate a distinctive, release-dependent form of facilitation in which probability of vesicle release is higher following a successful glutamate release event than following a failure. This phenomenon has an onset of ≤500ms and lasts several seconds, resulting in clusters of successful release events. The release-dependent facilitation requires neuronal contact with astrocytes and astrocytic glutamate uptake by EAAT1. It is not observed in neurons grown alone or in the presence of astrocyte-conditioned media. This form of facilitation dynamically amplifies multi-vesicular release. Facilitation-evoked release events exhibit spatial clustering and have a preferential localization toward the active zone center. These results uncover a rapid astrocyte-dependent form of facilitation acting via modulation of multi-vesicular release and displaying distinctive spatiotemporal properties.

Impact of past mining on public safety: seismicity in area of flooded abandoned coal Gardanne mine, France

This paper focuses on the impact of past mining on public safety. It emphasizes the need to understand the induced seismic hazard and consequently improve the post-mining management procedures and legislations, as many mining sites are located in proximity to populated areas. Due to many challenges and complexity of the post-mining environments, induced seismic hazard nowadays remains largely unknown. However, the return experience of several post-mining sites in recent decades have shown us that the mine flooding and/or degradation of mining works can lead to the stress perturbations, inducing the seismicity and the reactivation of the surrounding geological faults. Hence, it is important to advance the seismic monitoring and research of seismicity in flooded post-mining districts. As the number of mine closures worldwide is rising, it can be expected that flooding induced reactivation of the surrounding faults becomes a more often observed phenomenon. We present in this paper the experience of the abandoned flooded coal mine of Gardanne in France, which has been experiencing post-mining seismicity problems since its closure in 2010. We show the results of a recent study of seismic multiplets and clustering of seismic events, as well as their spatio-temporal activity compared to meteorological conditions. These results provide us new insights as well as lead to raising new questions on seismic sources and triggering mechanisms.

Association of Bradycardia and Asystole Episodes with Dialytic Parameters: An Analysis of the Monitoring in Dialysis (MiD) Study.

Bradycardia and asystole events are common among patients treated with maintenance hemodialysis. However, triggers of these events in patients on maintenance hemodialysis (HD), particularly during the long interdialytic period when these events cluster, are uncertain. The Monitoring in Dialysis Study (MiD) enrolled 66 patients on maintenance HD who were implanted with loop recorders and followed for 6 months. We analyzed associations of predialysis laboratory values with clinically significant bradyarrhythmia or asystole (CSBA) during the 12 hours before an HD session. Associations with CSBA were analyzed with mixed-effect models. Adjusted negative binomial mixed-effect regression was used to estimate incidence rate ratios (IRR) for CSBA. We additionally evaluated associations of CSBA at any time during follow-up with time-averaged dialytic and laboratory parameters and associations of peridialytic parameters with occurrence of CSBA from the start of one HD session to the beginning of the next. There were 551 CSBA that occurred in the last 12 hours of the interdialytic interval preceding 100 HD sessions in 12% of patients and 1475 CSBA events in 23% of patients overall. We did not identify significant associations between dialytic parameters or serum electrolytes and CSBA in the last 12 hours of the interdialytic interval in adjusted analyses. Median time-averaged ultrafiltration rate was significantly higher in individuals without CSBA (9.8 versus 8, P=0.04). Use of dialysate sodium concentrations ≤135 (versus 140) mEq/L was associated with a reduced risk of CSBA from the start of one session to the beginning of next. Although a few factors had modest associations with CSBA in some analyses, we did not identify any robust associations of modifiable parameters with CSBA in the MiD Study. Further investigation is needed to understand the high rates of arrhythmia in the hemodialysis population.

Associations between patterns of participation in community activities and social support, self-efficacy, self-rated health among community-dwelling older adults

Certain types of community-based social activities improve the health issues of older adults; however, the present patterns of participation in community activities remain unknown. This study aims 1) to identify community-dwelling older adults' patterns of participation in community-based activities and 2) to evaluate the relationships between social support, self-efficacy, self-rated health, and the patterns of participation in community activities. This cross-sectional study used data collected from 146 older adults aged ≥65 years who participated in community events in Japan in 2018. Cluster analysis was used to identify subjects' patterns of participation in community activities. In the multinomial logistic regression model, the participation pattern (dependent variable), and social support and self-efficacy (independent variables), were included, adjusting for age, sex, and years of residency. Three participation pattern clusters were identified: diverse activities (58%), municipal events (30%), and senior citizen club (12%). The proportion of participants reported themselves healthy were 93%, 88%, and 78% for diverse activities, municipal events, and senior citizen club clusters, respectively. Compared to those in the senior citizen club cluster, older adults in the diverse activities cluster were more likely to have self-efficacy (adjusted odds ratio (aOR): 1.19, p = .041) and social support (aOR: 2.35, p = .018), while participants in the municipal events cluster were associated with only social support (aOR: 3.29, p = .022). Increasing social support and self-efficacy may promote seniors’ participation in diverse community activities, which would be beneficial for their healthy aging. Further studies are needed to evaluate the causal relationships.

The Genomics of Industrial Process Through the Qualia of Markovian Behavior

A technique for registering and relating events that cause an observable and definable system state is proposed. Discrete events of system-state transfer are expressed by event tracking and clustering in the form of contiguous quanta of data. This approach is capable of describing typical processes in industrial systems in a chain of codes that contain system input/output parameters. The constituent nodes of the Markovian Processes chain form a series akin to genes in the deoxyribonucleic acid, repeatable and predictable. The process genes are the quanta of information that aligns to represent a chain of activities (process). They describe the causal links between occurring events forming a pattern (pathway) that leads to a well-specified output (e.g., a product with a defect or otherwise). The creation of process genomics requires the knowledge of system observed or latent parameters (state) as well as the state change at specified time intervals (discretization). The process genomics theory is tested in an industrial case study for quality assessment and control of glue dispensing in micro-semiconductor manufacturing. The resulting definitions of the system state and interrelationship of control parameters contribute to the development of the process genes. The outcome of the gene alignment is the geometric interpretation of the glue droplet formation. A predicted or observed droplet within the production tolerance leads to a nondefective product. The principle of creating production genomics is to find and rectify the defect-causing genes or to disrupt the sequences that lead to producing defective products, leading to a zero-defect manufacturing process.

On the temporal clustering of European extreme precipitation events and its relationship to persistent and transient large-scale atmospheric drivers

Extreme precipitation events that occur in close succession can have important societal and economic repercussions. Here we use 42 years of reanalysis data (ERA-5) to investigate the link between Euro-Atlantic large-scale pattern of weather and climate variability and the temporal clustering of extreme rainfall events over Europe. We implicitly model the seasonal rate of extreme occurrences as part of a Poisson General Additive Model (GAM) using cyclic regression cubic splines. The smoothed seasonal rate of extreme rainfall occurrences is used to (i) infer the frequency of significant temporal clustering and (ii) implicitly serves as the baseline rate when modeling the effects of atmospheric drivers on extreme rainfall clustering. We use GAMs to model the association between the temporal clustering of extreme rainfall events and seven predominant year-round weather regimes in the Euro-Atlantic sector as well as a measure of synoptic-scale transient recurrent Rossby wave packets. Sub-seasonal clustering of precipitation events is significant at all grid-points over Europe; the proportion of extreme rainfall events that cluster in time ranges between 2% to 27%. The most relevant weather regime is the Atlantic Trough (corresponding to NAO+ with a southward shift of the jet) explaining most of the significant increase in clustering probability over Europe. The Greenland Blocking regime explains most of the clustering over the Iberian Peninsula. The Scandinavian Blocking regime is associated with a significant increase in clustering probability over the western Mediterranean, with a northwards shift in the signal to central Europe in summer.

A Global Catalog of Calibrated Earthquake Locations

Abstract We produced a globally distributed catalog of earthquakes and nuclear explosions with calibrated hypocenters, referred to as the Global Catalog of Calibrated Earthquake Locations (GCCEL). This dataset currently contains 18,782 events in 289 clusters with >3.2 million arrival times observed at 19,258 stations. The term “calibrated” refers to the property that the hypocenters are minimally biased by unknown Earth structure. In addition, we calculate uncertainties using empirically determined variability of the arrival-time data itself, specific to each calibrated cluster of hypocenters. Outliers in the arrival-time dataset are removed based on measured variability of the data. In each cluster, we estimate the empirically determined uncertainty for each set of station-phase arrival times. We use a version of the hypocentroidal decomposition multiple event relocation algorithm specifically adapted for calibrated relocations of clusters of seismic events. Most clusters are calibrated by fitting the subset of direct crustal first arrivals (Pg and Sg) with a locally appropriate travel-time model to estimate the cluster hypocentroid. A few clusters are calibrated by aligning the pattern of relative locations in space and time with one or more events for which a ground-truth hypocenter is available from an independent source with known uncertainty, such as a nuclear explosion. Epicentral uncertainties in GCCEL typically range from 1 to 5 km with a 90% confidence interval. Most events have depth constraint from one or more sources, usually with an uncertainty of ≤5 km. GCCEL is a significant resource for research at local, regional, and global scales because it provides minimally biased absolute hypocenters, meaningful associated error estimates, and curated arrival times as a reference dataset that can be used as prior constraints in the development of new regional, national, and global earthquake catalogs; validation of new location techniques; and the generation of advanced Earth models.

Meteorological factor contributions to the seesaw concentration pattern between PM2.5 and O3 in Shanghai

With the implementation of various stringent emission reduction measures since 2013 in China, significant declines in fine particle (PM2.5) concentrations have occurred nationwide. However, China has suffered from increasing levels of ozone pollution in eastern urban areas. Many studies focus on the chemical interaction between PM2.5 and O3, but the meteorological mechanisms of the seesaw variation pattern between them are still unclear. Taking the megacity Shanghai (SH) as an example, we explored the meteorological causes of two types of PM2.5-O3 concentration variation seesaw events, i.e., high PM2.5 concentration with low O3 concentration (Type-one) events and low-PM-high-O3 (Type-two) seesaw events. The backward trajectories of the 144 Type-one events are divided into three clusters. Among the three clusters of Type-one seesaw events, the boundary layer height decreases by 20.53%–53.58%, and the wind speed decreases by 17.99%–28.29%, which is unfavorable for the diffusion of local air pollutants and contributes to the accumulation of PM2.5. Additionally, a backward air mass with a high content of PM2.5 plays an important role in the Type-one events, especially in the case of cluster one. In terms of cluster two, the increase in cloud cover, decrease in solar radiation and increase in relative humidity also promote the hygroscopic growth of aerosols and suppress the production of O3. As for cluster three, higher cloud cover and relative humidity contribute to the seesaw pattern of PM and O3. The 64 Type-two seesaw events are divided into two clusters. O3-rich air masses from the nearby east sea surface and remote northeast China increase the local O3 of SH. Moreover, in cluster one, high boundary layer depth and wet deposition contribute to the decrease in PM2.5 concentration. In cluster two, a obvious decrease in cloud cover and increase in solar radiation are also favorable for the photochemical production of ozone. The results will provide suggestions for the government to use to take measures to improve the air quality of SHs.

Development of a method for targeted monitoring and processing of information security incidents of economic entities

Based on the analysis of methods for monitoring information security (IS) of economic entities (EDMS), a method for targeted monitoring and processing of IS incidents is proposed, including procedures for fuzzy clustering of IS events, and taking into account the complexity of processing the initial monitoring data and the performance of allocated resources for processing IS incidents. To assess the effectiveness of this method, the article presents the results of computer modeling using a statistical model for monitoring IS EDMS.

Compound flood hazard at Lake Como, Italy, is driven by temporal clustering of rainfall events

Lake floods occur when the water level in the lake exceeds a threshold causing inundation of neighbouring shorelines. Despite the potential impacts of this type of flood on neighbouring settlements, the mechanisms and drivers that govern when lake floods occur, and particularly how they result from compound factors, remains poorly understood. Here we compile and analyze meteorological and historical data on lake floods at Lake Como (northern Italy) between 1980 and 2020. We identify seven modes of lake floods with climate-based drivers. In 70% of cases, floods are associated with a temporal clustering of rainfall. This was also the predominant trigger of the seven most severe floods. To a lesser extent, floods were driven by a single rainfall event over a water level previously increased by rainfall and/or melting. We conclude that lake floods represent a clear example of the potential for compound mechanisms to govern and exacerbate hazards.

NATURAL DISASTER EVENT MAPPING IN WEST JAVA USING K-MEANS ALGORITM

<p><em> Natural disaster is an event that cannot be avoided, therefore a mapping of occurrence of the natural disaster is needed. In additional to mapping, clustering of natural disaster events is also needed to determine which areas have low to high intensity events. In performing the clustering, a method or algorithm can be used, namely the k-means algorithm.</em></p><p><em>In the research conducter, the scope of natural disasters is West Java Province with the attributes used are floods, landslides and tornadoes. And also from this research, it was conducted to find out how many optimal number of clusters that can be clustered.</em></p><p><em>The method that used in this study is the K-Means algorithm which is used to perform clustering. The Elbow method is used to determine the optimal K value from the dataset by calculate the SSE (Sum Square Error) of each predetermined cluster.</em></p><p><em>From the result of its application, the K-Means algorithm can cluster datasets of Natural Disaster in West Java with predetermined attributes. Based on the calculation results from the elbow method, the value of K from the dataset is 4. And from the research conducter, the accuracy rate of each cluster is 0,04% to 0,56%.</em></p><p><strong><em>Keywords: </em></strong><em> </em><em>K-Means, Natural Disaster, Elbow Method, Cluster, Machine Learning</em></p>

Models and methods for assessing the security of an informatization object

The paper substantiates the need of creation an information decision support system in the development of systems for protecting informatization objects. Analyzes of existing systems in various fields of activity, the requirements for the functionality of the system in relation to the field of information protection, methods for developing models of functioning of protected information systems in a destructive environment impact on the basis of Bayesian networks are considered. The paper gives a description of a typical module functioning of this model. The structures of probabilistic models of the relationship of vulnerabilities, information security threats, methods and scenarios for their implementation, the formation of measures to protect informatization objects, the formation and assessment of the risks of incidents and their damage are considered. Clusters of typical information security events, methodological apparatus for calculating the joint distribution of the probabilities of protective and destructive events are determined. Finally, typical chains of interconnections of such events are identified. Mathematical apparatus for calculating their probabilities, a verbal description of the patterns of their mutual influence, and a method for converting quantitative probabilistic values of informatization object security indicators into qualitative ones are presented, and the results of the study are summarized.

Does privacy breach affect firm performance? An analysis incorporating event-induced changes and event clustering

Previous studies have analyzed the impact of cybersecurity breaches on firm performance, but the impact of the privacy breach on firm performance is less explored. Needless to say, the privacy of the individual's personal information has been a rising concern for organizations over the years. Previous studies, primarily focused on cybersecurity breaches, have used a simple market model (MM) to observe the impact of these breaches on firm performance. Our study has used an advanced market model (AMM), which observes the event-induced changes in the variance of stock returns and changes in MM parameters, which are ignored by the MM. The ignorance of this may lead to a biased cumulative abnormal return (CAR) computation. We have also included the event clustering observation (events that are very close to each other) in our study and evaluated AMM with seemingly unrelated regression (SUR), i.e., AMM-SUR. We have used data of 193 privacy breaches related to the US firms listed on NYSE (USA), NASDAQ (USA) for the years 2012 (43 breaches), 2013 (37 breaches), 2014 (51 breaches), and 2018 (62 breaches). Abnormal returns for the firms are found negative due to these privacy breaches, but AMM consistently reports more negative abnormal returns than MM for all the years. The AMM-SUR, observing event clustering, consistently reports slightly lower negative abnormal returns than the AMM for all the years. We have also calculated the average financial loss due to a privacy breach for these years and for three different models, i.e., MM, AMM, and AMM-SUR.

Statistical analyses in the case of an Italian nurse accused of murdering patients

Abstract Suspicions about medical murder sometimes arise due to a surprising or unexpected series of events, such as an apparently unusual number of deaths among patients under the care of a particular nurse. But also a single disturbing event might trigger suspicion about a particular nurse, and this might then lead to investigation of events which happened when she was thought to be present. In either case, there is a statistical challenge of distinguishing event clusters that arise from criminal acts from those that arise coincidentally from other causes. We show that an apparently striking association between a nurse’s presence and a high rate of deaths in a hospital ward can easily be completely spurious. In short: in a medium-care hospital ward where many patients are suffering terminal illnesses, and deaths are frequent, most deaths occur in the morning. Most nurses are on duty in the morning, too. There are less deaths in the afternoon, and even less at night; correspondingly, less nurses are on duty in the afternoon, even less during the night. Consequently, a full time nurse works the most hours when the most deaths occur. The death rate is higher when she is present than when she is absent.

Evidence of fast neutron detection capability of the CLLB scintillation detector

For the first time we report the ability of the CLLB scintillator to detect and discriminate fast neutrons (2–10 MeV energy range) from gamma-rays and thermal neutrons, using a digital pulse shape discrimination technique. Firstly, we conducted measurements in single mode with an Am–Be source, unshielded and shielded, and we tagged a new cluster of events as the fast neutron-induced zone. The Figure of Merit obtained for the fast neutron/gamma discrimination was 1.1. Then, in order to better characterize these new cluster of events induced by fast neutrons, we conducted measurements in coincidence mode. We used a mono-energetic pulsed neutron source, at the Van de Graaff accelerator facility at the Legnaro National Laboratories (INFN-LNL, Legnaro-Italy), and also, a continuous neutron spectrum (using a 252Cf source). Combining all experimental results with a Monte Carlo model of the CLLB detector, built using the GEANT4 tool kit, it was possible to obtain the light output function of 7Li ions in the CLLB crystal (dL/dE = κ; κ = 0.36 ± 0.02). Moreover, the intrinsic fast neutron efficiency was modeled by Monte Carlo, obtaining similar values 1.5%–2% for neutrons between 2 MeV and 10 MeV. Finally, an application of this new feature, in the field of nuclear security and nuclear safeguards, is given.

Fractal Seismicity and Coulomb Stress Pattern Analysis for 7 January 2020 Mw 6.4 Puerto Rico Earthquake

Abstract This study aims to understand the variation of the correlation fractal dimension and static stress changes associated with the 2019 Puerto Rico sequence with special reference to the January 2020 Mw 6.4 mainshock. On examining the seismicity data from 1988 to 2020, we find a shift in the seismicity pattern after the 2019 Puerto Rico sequence. A detailed examination of changes in the correlation fractal dimension (Dc) indicates diffused seismicity throughout the study region until the year 2003 that is demonstrated by relatively high Dc values. Toward the end of 2019, a drastic drop in Dc is observed; it is associated with a tight clustering of events to the south-southwest of Puerto Rico Island (PRI), suggesting the onset of the 2019 sequence. We observed a persistent clustering and significant drop in Dc before the Mw 6.4 earthquake, hinting at the presence of a numerical precursor for this event. Over 30 yr of historical earthquakes have contributed positive Coulomb stress distributions mainly in the northern region of PRI. A deflection in this pattern was reflected after the 2019 Puerto Rico sequence. Based on the static stress analysis, we demonstrated that the Mw 6.4 mainshock was triggered by the 2019 Puerto Rico sequence. The new cluster of events, which contains the Mw 6.4 mainshock, exhibits higher stress levels in the south-southwest region of PRI. The eastern end of Septentrional fault (SF), the zone between SF and North Puerto Rico Slope fault, the middle segment of South Puerto Rico Slope fault, South Lajas fault, Punta Montalva fault, and Central fault are the highly stressed regions at present. Stress perturbation induced by the destructive Mw 6.4 event on the adjacent fault system is a potential threat for PRI, where population density is very high. The combined analysis of Dc and static stress changes indicates the presence of a numerical precursor and highly stressed regions southwest of PRI.

Child language and autism diagnosis impact hierarchical temporal structure of parent-child vocal interactions in early childhood.

Timing is critical to successful social interactions. The temporal structure of dyadic vocal interactions emerges from the rhythm, timing, and frequency of each individuals' vocalizations and reflects how the dyad dynamically organizes and adapts during an interaction. This study investigated the temporal structure of vocal interactions longitudinally in parent-child dyads of typically developing (TD) infants (n=49; 9-18 months; 48% male) and toddlers with ASD (n=23; 27.2 ± 5.0months; 91.3% male) to identify how developing language and social skills impact the temporal dynamics of the interaction. Acoustic hierarchical temporal structure (HTS), a measure of the nested clustering of acoustic events across multiple timescales, was measured in free play interactions using Allan Factor. HTS reflects a signal's temporal complexity and variability, with greater HTS indicating reduced flexibility of the dyadic system. Child expressive language significantly predicted HTS (ß=-0.2) longitudinally across TD infants, with greater dyadic HTS associated with lower child language skills. ASD dyads exhibited greater HTS (i.e., more rigid temporal structure) than nonverbal matched (d=0.41) and expressive language matched TD dyads (d=0.28). Increased HTS in ASD dyads occurred at timescales >1s, suggesting greater structuring of pragmatic aspects of interaction. Results provide a new window into how language development and social reciprocity serve as constraints to shape parent-child interaction dynamics and showcase a novel automated approach to characterizing vocal interactions across multiple timescales during early childhood.

High-resolution Bayesian mapping of landslide hazard with unobserved trigger event

Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution. However, the spatial distribution of the triggering event (e.g., precipitation or earthquakes) is often not directly observed. In this paper we develop Bayesian spatial hierarchical models for point patterns of landslide occurrences using different types of log-Gaussian Cox processes. Starting from a competitive baseline model that captures the unobserved precipitation trigger through a spatial random effect at slope unit resolution, we explore novel complex model structures that take clusters of events arising at small spatial scales into account as well as nonlinear or spatially-varying covariate effects. For a 2009 event of around 5000 precipitation-triggered landslides in Sicily, Italy, we show how to fit our proposed models efficiently, using the integrated nested Laplace approximation (INLA), and rigorously compare the performance of our models both from a statistical and applied perspective. In this context we argue that model comparison should not be based on a single criterion and that different models of various complexity may provide insights into complementary aspects of the same applied problem. In our application our models are found to have mostly the same spatial predictive performance, implying that key to successful prediction is the inclusion of a slope-unit resolved random effect capturing the precipitation trigger. Interestingly, a parsimonious formulation of space-varying slope effects reflects a physical interpretation of the precipitation trigger: in subareas with weak trigger, the slope steepness is shown to be mostly irrelevant.