Classes Of Events Research Articles

Machine Learning Approach for Event Position Reconstruction in the DEAP-3600 Dark Matter Search Experiment

In addition to classical analytical data processing methods, machine learning methods are widely used for data analysis in elementary particle physics. Most often, such techniques are used to identify a particular class of events (the classification problem) or to predict a certain event parameter (the regression problem). Here, we present the result of using a machine learning model to solve the regression problem of event position reconstruction in the DEAP-3600 dark matter search detector. A neural network was used as a machine learning model. Improving the position resolution will improve the reduction in background events, while increasing the signal acceptance for weakly interacting massive particles.

Exploratory study of the EU-DEMO Water-Cooled Lithium Lead breeding blanket behaviour in case of loss of cooling capability

Within the framework of the European Roadmap to the realization of fusion energy, a strong international cooperation is ongoing to develop a Breeding Blanket (BB) system for the EU-DEMO reactor. Although it is still to be decided whether the DEMO in-vessel components should perform any safety function, the pursuing of robust blanket concepts able to handle upset and accidental loading conditions has been always seen as good practice in fusion reactor engineering to enhance the inherent plant safety performances. Amongst the several classes of events that might challenge the BB structural integrity, the large Loss of Coolant Accident is one of the most relevant because it usually leads to a fast loss of cooling capability of the structures. Due to the characteristic of the tokamak assembly, the behaviour of each blanket segment during a sudden loss of cooling capability does not depend only upon distinguishing features of the component itself. In fact, the overall transient can be governed by conditions established in surrounding elements, like adjacent blanket segments and vacuum vessel, as well as by the plasma shutdown strategies adopted to protect the reactor.The scope of the activity herein presented is to make a preliminary assessment of the intrinsic capability of EU-DEMO tokamak architecture to cope with the loss of cooling in the Water-Cooled Lithium Lead (WCLL) BB concept. Evaluation of BB thermal field in short and medium term under simplified, yet conservative, assumptions was carried out for four transient scenarios with the aim of investigating the response of the structure in case of: a) fast or soft plasma shutdown, and b) different blanket cooling schemes. Moreover, the WCLL BB thermo-mechanical response in the most critical time steps has been assessed. The obtained results shall help for future decisions on safety systems/action to be implemented to cope with accidents.

A metric-learning method for few-shot cross-event rumor detection

With the rapid development of social media, quickly detecting rumors on social media has become vitally crucial. However, there exists the following challenges. 1) Rumors are always flooded with time-critical events, where large-scale labeled datasets are difficult to obtain. 2) Although historic events have sufficient labels, the performance of models tends to degrade on the newly emergent events due to event difference shift. Facing these challenges, in this study, we attempt to leverage the idea of few-shot learning which aims to quickly acquire knowledge on unseen events with a few labeled samples by sufficiently learning from old events with a number of verified samples. Different from few-shot learning tasks in the literature, rumors in the same class of different events (no matter historical events or new events) are likely to contain coincident features in their embedding space. Therefore, we validate two classical metric learning methods, the prototypical network (PN) and the relation network (RN) which are able to capture the class-level representations in few-shot learning settings, to explore the effectiveness of metric learning methods for cross-event rumor detection. Our proposed model contains two stages corresponding to Base-Classifier pre-trained module and Base-Meta training module. The Base-Classifier pre-trained module is a classification model trained on old events. Then its last layer for class prediction is removed and the remains of the module is viewed as an encoder. The Base-Meta training module fine-tunes the encoder, meanwhile trains a selected metric learning model with episodic training strategies on old events. Empirical tests on novel events have showed that our model can outperform the state-of-the-art baseline models on the benchmark cross-event rumor datasets PHEME5 and PHEME9. What’s more, the model can improve the performance of cross-data rumor detection, where the model is trained on Twitter15, but tested on Twitter16.

Automated classification of seismic signals recorded on the Åknes rock slope, Western Norway, using a convolutional neural network

Abstract. A convolutional neural network (CNN) was implemented to automatically classify 15 years of seismic signals recorded by an eight-geophone network installed around the back scarp of the Åknes rock slope in Norway. Eight event classes could be identified and are adapted from the typology proposed by Provost et al. (2018), of which five could be directly related to movements on the slope. Almost 60 000 events were classified automatically based on their spectrogram images. The performance of the classifier is estimated to be near 80 %. The statistical analysis of the results shows a strong seasonality of the microseismic activity at Åknes with an annual increase in springtime when snow melts and the temperature oscillates around the freezing point, mainly caused by events within classes of low-frequency slope quakes and tremors. The clear link between annual temperature variations and microseismic activity could be confirmed, supporting thawing and freezing processes as the origins. Other events such as high-frequency and successive slope quakes occur throughout the year and are potentially related to the steady creep of the sliding plane. The huge variability in the annual event number cannot be solely explained by average temperatures or varying detectability of the network. Groundwater recharge processes and their response to precipitation episodes are known to be a major factor of sliding at Åknes, but the relationship with microseismic activity is less obvious and could not be demonstrated.

Emergence of extreme events in a quasiperiodic oscillator.

Extreme events are unusual and rare large-amplitude fluctuations can occur unexpectedly in nonlinear dynamical systems. Events above the extreme event threshold of the probability distribution of a nonlinear process characterize extreme events. Different mechanisms for the generation of extreme events and their prediction measures have been reported in the literature. Based on the properties of extreme events, such as those that are rare in the frequency of occurrence and extreme in amplitude, various studies have shown that extreme events are both linear and nonlinear in nature. Interestingly, in this Letter, we report on a special class of extreme events which are nonchaotic and nonperiodic. These nonchaotic extreme events appear in between the quasiperiodic and chaotic dynamics of the system. We report the existence of such extreme events with various statistical measures and characterization techniques.

Application of the global neutron monitor network for assessment of spectra and anisotropy and the related terrestrial effects of strong SEPs

Studies of the characteristic of strong solar energetic particles (SEPs), that is events that can be registered by ground-based instruments such as neutron monitors, allow one to understand their acceleration and propagation in interplanetary space and gives the necessary basis to quantify the related cosmic-ray-induced terrestrial effects. After solar eruptive processes, such as solar flares and/or coronal mass ejections, solar ions are accelerated to the high-energy range. In most cases, the energy of the accelerated solar ions reaches several tens of MeV/n, yet occasionally it exceeds 100 MeV/n or even reaches the GeV/n range. In the latter case, the energy of SEPs is enough, so that they induce an atmospheric particle shower, whose secondary particles reach the ground, eventually registered by ground-based detectors, specifically neutron monitors. This particular class of events is known as ground-level enhancements (GLEs). Here we present a full-chain scheme for assessment of spectra and anisotropy and the related terrestrial effects of GLE causing SEPs. We discuss the application of a method for analysis of GLEs using neutron monitor data and the employment of the derived spectra as a basis to quantify cosmic-ray-induced terrestrial effects during GLEs, specifically atmospheric ionization and exposure to secondary radiation (radiation dose) at cruising aviation altitudes. We present an example study of two GLEs, namely their SEP energy/rigidity spectra derived using records from the global NM network and models for quantification of the atmospheric ionization and exposure to radiation at flight altitudes (ambient dose) during the considered events.

Automatic Identification and Geo-Validation of Event-Related Images for Emergency Management

In recent years, social platforms have become integrated in a variety of economic, political and cultural domains. Social media have become the primary outlets for many citizens to consume news and information, and, at the same time, to produce and share online a large amount of data and meta-data. This paper presents an innovative system able to analyze visual information shared by citizens on social media during extreme events for contributing to the situational awareness and supporting people in charge of coordinating the emergency management. The system analyzes all posts containing images shared by users by taking into account: (a) the event class and (b) the GPS coordinates of the geographical area affected by the event. Then, a Single Shot Multibox Detector (SSD) network is applied to select only the posted images correctly related to the event class and an advanced image processing procedure is used to verify if these images are correlated with the geographical area where the emergency event is ongoing. Several experiments have been carried out to evaluate the performance of the proposed system in the context of different emergency situations caused by earthquakes, floods and terrorist attacks.

Sound event triage: detecting sound events considering priority of classes

We propose a new task for sound event detection (SED): sound event triage (SET). The goal of SET is to detect an arbitrary number of high-priority event classes while allowing misdetections of low-priority event classes where the priority is given for each event class. In conventional methods of SED for targeting a specific sound event class, it is only possible to give priority to a single event class. Moreover, the level of priority is not adjustable, i.e, the conventional methods can use only types of target event class such as one-hot vector, as inputs. To flexibly control much information on the target event, the proposed SET exploits not only types of target sound but also the extent to which each target sound is detected with priority. To implement the detection of events with priority, we propose class-weighted training, in which loss functions and the network are stochastically weighted by the priority parameter of each class. As this is the first paper on SET, we particularly introduce an implementation of single target SET, which is a subtask of SET. The results of the experiments using the URBAN–SED dataset show that the proposed method of single target SET outperforms the conventional SED method by 8.70, 6.66, and 6.09 percentage points for “air_conditioner,” “car_horn,” and “street_music,” respectively, in terms of the intersection-based F-score. For the average score of classes, the proposed methods increase the intersection-based F-score by up to 3.37 percentage points compared with the conventional SED and other target-class-conditioned models.

Paradigma Pembangunan Ekonomi Konvensional dan Pembangunan Ekonomi Islam

This research is entitled the different paradigms of conventional economic development and Islamic economic development. The background of this research is that there are differences between Islamic economics and conventional economics in the field of development both from the aspects of methodology, principles, principles and scope. For this reason, researchers conducted a literature study on the differences in the paradigms of conventional economic development and Islamic economic development. The method used is descriptive analysis method, which is a method that examines the status of human groups, objects, conditions, a system of thought, or a class of events at the present time with the aim of making systematic, factual and accurate descriptions, drawings or drawings of facts, nature, as well as the relationship between the phenomena investigated. So this study resulted in findings that: the paradigm (worldview) that underlies Islamic development economics is different from conventional economics. Islamic development economics is formed based on the Islamic worldview which places the main guide in carrying out life activities (including economic activities both individually and as entities or groups) originating from the Al-Qur'an and Al-Hadith. Conventional development economics has fundamental differences with Islamic development economics. Conventional development economics has not been able to fully meet the needs of Islamic society in terms of knowledge, fulfillment of basic needs, and accommodating the basic motives of economics so that the presence of Islamic economic development is indispensable

Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis

Abstract. Water ponding and pluvial flash flooding (PFF) on roadways can pose a significant risk to drivers. Furthermore, climate change, growing urbanization, increasing imperviousness, and aging stormwater infrastructure have increased the frequency of these events. Using physics-based models to predict pluvial flooding at the road segment scale requires notable terrain simplifications and detailed information that is often not available at fine scales (e.g., blockage of stormwater inlets). This brings uncertainty into the results, especially in highly urbanized areas where micro-topographic features typically govern the actual flow dynamics. This study evaluates the potential for flood observations collected from Waze – a community-based navigation app – to estimate the likelihood of PFF at the road segment scale. We investigated the correlation of the Waze flood reports with well-known flood observations and maps, including the National Flood Hazard Layer (NFHL), high watermarks, and low water crossings data inventories. In addition, highly localized surface depressions and their catchments are derived from a 1 m resolution bare-earth digital elevation model (BE-DEM) to investigate the spatial association of Waze flood reports. This analysis showed that the highest correlation of Waze flood reports exists with local surface depressions rather than river flooding, indicating that they are potentially useful indicators of PFF. Accordingly, two data-driven models, empirical Bayes (EB) and random forest (RF) regression, were developed to predict the frequency of flooding, a proxy for flood susceptibility, for three classes of historical storm events (light, moderate, and severe) in every road segment with surface depressions. Applying the models to Waze data from 150 storms in the city of Dallas showed that depression catchment drainage area and imperviousness are the most important predictive features. The EB model performed with reasonable precision in estimating the number of PFF events out of 92 light, 41 moderate, and 17 severe storms with 0.84, 0.85, and 1.09 mean absolute errors, respectively. This study shows that Waze data provide useful information for highly localized PFF prediction. The superior performance of EB compared to the RF model shows that the historical observations included in the EB approach are important for more accurate PFF prediction.

Rowing in Los Angeles: Performance Considerations for the Change to 1500m at the 2028 Olympic Games.

World Rowing's decision to support the proposed change from a 2000-m to a 1500-m regatta course at the 2028 Olympic Games in Los Angeles is anticipated to have important implications for athlete preparation and race execution during the 2024-2028 quadrennium. This commentary aims to provide insight into the expected implications of the reduction in course length heading into the 2028 Games, focusing on the training and monitoring of high-performance rowers, as well as tactical, technical, and pacing considerations for performance. The reduction in event duration (estimated to be ∼90-120s across all event classes) will lead to an expected ∼5% to 15% increase in relative contribution of anaerobic metabolism. Consequently, adjustment in training periodization priorities toward higher-intensity interventions may be required, especially in the period immediately prior to the games. The critical-power and anaerobic-power-reserve concepts may become more useful tools for structuring exercise programs, evaluating training outcomes, and determining event suitability through individual physiological profiling. Additionally, the adoption of a more constant (flat) pacing strategy, rather than the commonly used reverse J-shaped approach, might be considered for racing over this new distance. Finally, technical aspects, such as stroke rate and gearing, may require adjustment for optimal performance; however, research is clearly required to explore such effects. Our intention is to stimulate discussion and debate, with the provision of practical recommendations that aim to optimize rowers' preparation for and performance at the 2028 Olympic Games.

Complex Event Recognition From Discrete Sensor Data With a Discrete Event System Framework

Recognizing complex events revealed by sensor readings is an increasingly crucial task that serves as one of the foundations for system monitoring and decision-making. In this paper, we investigate the recognition problem for a class of complex events that can be represented by a sequence of discrete sensor outputs. We view the outputs of discrete sensors as sensor events. We use an automaton to describe all possible sequences of sensor events that can be generated in a given system. The complex events to be recognized is a set of sequences of sensor events and can be represented by the marked language of another automaton. For a given sensor event sequence, we introduce the notion of “partition” to stand for a possible complex event sequence. By constructing an augmented automaton that includes all possible partitions, we find a necessary and sufficient condition for the recognition problem to be solvable. We then find an algorithm to check the condition. Finally, an online complex event recognition procedure is proposed to determine the occurred complex events when the complex event recognition problem is solvable.

An Analysis on Students’ Ability in Writing Descriptive Text

English is the language used by almost everyone. In Indonesia, English is the first foreign language and a required subject in schools. Nowadays, not only a foreign language but also taught from secondary up to university. Learning English aims to master the four basic language skills of listening, speaking, reading, and writing. Students also begin to interact with others at the college level, they begin to learn to communicate in writing (Rianti 2016). In this section, the author focuses on writing. This research is a descriptive method. According to Nazir (1988:63) says that Descriptive method is a method of researching the status of a group of humans, an object, a set of conditions, a system of thought, or a class of events in the present. The purpose of this descriptive study was to create a description, picture, or painting in systematic, factual, and accurate information on the facts, properties, and relationships among the phenomena investigated. This research was conducted in SMA 2 Bangkinang Kota, located in Abdul Shaleh N0. 55, Bangkinang, Kampar, Riau. The total of students is 28. They consist of 12 males and 16 females. The main concern of this research was focused on the Analysis of students'' writing skills of descriptive text in the second grade of SMA 2 Bangkinang Kota. In this discussion of the result of students'' scores on writing ability of descriptive text. The result of the tests for the students was fair. Score the range showed that the mean score of test 1. The test of the students is categorized as good and fair. It means the level of excellence is no students or 0 % students. The level good is 8 or 32 % students. The fair level is 20 or 80 % students. From the discussion above the level of students in the test are fair. The student''s problems in writing skills of descriptive text are the student''s lack of vocabulary, which indicates the students did not bring a dictionary during when learning process and the students think that English vocabulary is hard to be learned. The students should always bring a dictionary when English learning. As already mentioned in the first chapter, the aim of this study are to describe students’ ability in writing a descriptive text focused on Content, Grammar, Vocabulary, Organization, and Mechanics in the Second Grade of SMA 2 Bangkinang Kota. The researcher would like to conclude that 28 students at XI IPA 3 in SMA 2 Bangkinang are fair in writing descriptive text. Based on the data presentation and the previous chapter, the researcher gets a conclusion of the research. The students'' writing skill in the descriptive text is fair. The result shows the mean score of the test is 69. It is classified that the students writing skills in the descriptive text of test are fair to level.

Sound Event Detection System Based on VGGSKCCT Model Architecture with Knowledge Distillation

ABSTRACT Sound event detection involves detecting acoustic events of multiple classes in audio recordings, along with the times of occurrence. Detection and Classification of Acoustic Scenes and Events (DCASE) Task 4 for sound event detection in domestic environments is a contest on this task. In this paper, we engineer sound event detection systems using the data provided and the performance metrics defined in this contest. Note the performance metrics of polyphonic sound detection scores (PSDS) in 2 scenarios are adopted recently to be practical and effective. Our system development started with a basic system through reference to various systems in the contests of previous years. We developed a system similar to that used by the winning team in DCASE Challenge 2021. A clip-level consistency branch is then added to the model architecture to increase the performance of the PSDS in scenario 2, which focuses on identifying different event classes. In addition, we use knowledge distillation with the mean teacher model to improve system performance. In this way, the model can learn from the pre-trained model without being fully restricted by its performance. Finally, we further enhance the system robustness through consistency criteria in the second stage of training. On the official validation set of Domestic Environment Sound Event Detection (DESED) dataset, our final system achieves 0.418 and 0.661 on the PSDS in the two scenarios. It outperforms the 2021 baseline system with 0.341 and 0.546 on both scores quite significantly.

Deep Attention-based Supernovae Classification of Multiband Light Curves

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multiband light curves is a challenging task due to the highly irregular cadence, long time gaps, missing values, few observations, etc. These issues are particularly detrimental to the analysis of transient events: SN-like light curves. We offer three main contributions: (1) Based on temporal modulation and attention mechanisms, we propose a deep attention model (TimeModAttn) to classify multiband light curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-value assumptions, and explicit imputation/interpolation methods. (2) We propose a model for the synthetic generation of SN multiband light curves based on the Supernova Parametric Model, allowing us to increase the number of samples and the diversity of cadence. Thus, the TimeModAttn model is first pretrained using synthetic light curves. Then, a fine-tuning process is performed. The TimeModAttn model outperformed other deep learning models, based on recurrent neural networks, in two scenarios: late-classification and early-classification. Also, the TimeModAttn model outperformed a Balanced Random Forest (BRF) classifier (trained with real data), increasing the balanced-F 1score from ≈.525 to ≈.596. When training the BRF with synthetic data, this model achieved a similar performance to the TimeModAttn model proposed while still maintaining extra advantages. (3) We conducted interpretability experiments. High attention scores were obtained for observations earlier than and close to the SN brightness peaks. This also correlated with an early highly variability of the learned temporal modulation.

Internal mechanical dissipation mechanisms in amorphous silicon

Using activation-relaxation technique nouveau (artn), we search for two-level systems (TLSs) in models of amorphous silicon ($a$-Si). The TLSs are mechanisms related to internal mechanical dissipation and represent the main source of noise in the most sensitive frequency range of the largest gravitational wave detectors as well as one of the main sources of decoherence in many quantum computers. We show that in $a$-Si, the majority of the TLSs of interest fall into two main categories: bond-defect hopping, where neighbors exchange a topological defect, and Wooten-Winer-Weaire bond exchange. The distribution of these categories depends heavily on the preparation schedule of the $a$-Si. We use our results to compute the mechanical loss in amorphous silicon, leading to a loss angle of ${10}^{\ensuremath{-}3}$ at room temperature, decreasing to ${10}^{\ensuremath{-}4}$ at 150 K in some configurations. Our modeling results indicate that multiple classes of events can cause experimentally relevant TLSs in disordered materials and therefore multiple attenuation strategies might be needed to reduce their impact.

A nearby long gamma-ray burst from a merger of compact objects

Gamma-ray bursts (GRBs) are flashes of high-energy radiation arising from energetic cosmic explosions. Bursts of long (greater than two seconds) duration are produced by the core-collapse of massive stars1, and those of short (less than two seconds) duration by the merger of compact objects, such as two neutron stars2. A third class of events with hybrid high-energy properties was identified3, but never conclusively linked to a stellar progenitor. The lack of bright supernovae rules out typical core-collapse explosions4–6, but their distance scales prevent sensitive searches for direct signatures of a progenitor system. Only tentative evidence for a kilonova has been presented7,8. Here we report observations of the exceptionally bright GRB 211211A, which classify it as a hybrid event and constrain its distance scale to only 346 megaparsecs. Our measurements indicate that its lower-energy (from ultraviolet to near-infrared) counterpart is powered by a luminous (approximately 1042 erg per second) kilonova possibly formed in the ejecta of a compact object merger.

On removal of one undetected paradox in foundations of probability theory

The paper points out a logical contradiction that arises in the modern approach to teaching the foundations of the probability theory. This contradiction is of the following form: \U0001d74e = {\U0001d74e}, where \U0001d74e is an arbitrary elementary outcome. In modern mathematics the formation of expressions of the form \U0001d499 = {\U0001d499} is unacceptable. Here {\U0001d499} is a unit set, i.e., a set whose sole element is some element \U0001d499 of an arbitrary nature. This paradox \U0001d74e = {\U0001d74e} is a consequence of the fact that random events and between them also elementary outcomes \U0001d74e are considered as some subsets in the set of all elementary outcomes. It is shown how this contradiction can be easily eliminated by applying the axiomatic method. It is based on the introduction of two simple axioms that impose restrictions on elementary outcomes and on the class of random events under consideration. In addition from these two axioms on the basis of strictly logical reasoning the authors derived the representation of an arbitrary random event in the form of a sum of elementary outcomes favorable to it. Moreover, another non-axiomatic approach to the elimination of this paradox is proposed. In the final part of the paper other variants of the second of the introduced axioms are considered as well.

ImpedanceVerif: On-Chip Impedance Sensing for System-Level Tampering Detection

Physical attacks can compromise the security of cryptographic devices. Depending on the attack’s requirements, adversaries might need to (i) place probes in the proximity of the integrated circuits (ICs) package, (ii) create physical connections between their probes/wires and the system’s PCB, or (iii) physically tamper with the PCB’s components, chip’s package, or substitute the entire PCB to prepare the device for the attack. While tamper-proof enclosures prevent and detect physical access to the system, their high manufacturing cost and incompatibility with legacy systems make them unattractive for many low-cost scenarios. In this paper, inspired by methods known from the field of power integrity analysis, we demonstrate how the impedance characterization of the system’s power distribution network (PDN) using on-chip circuit-based network analyzers can detect various classes of tamper events. We explain how these embedded network analyzers, without any modifications to the system, can be deployed on FPGAs to extract the frequency response of the PDN. The analysis of these frequency responses reveals different classes of tamper events from board to chip level. To validate our claims, we run an embedded network analyzer on FPGAs of a family of commercial development kits and perform extensive measurements for various classes of PCB and IC package tampering required for conducting different side-channel or fault attacks. Using the Wasserstein Distance as a statistical metric, we further show that we can confidently detect tamper events. Our results, interestingly, show that even environment-level tampering activities, such as the proximity of contactless EM probes to the IC package or slightly polished IC package, can be detected using on-chip impedance sensing.

Towards rainstorm event identification: A transfer learning framework using citizen-report texts and multi-source spatial data

Urban rainstorms are often accompanied by various hazardous events (e.g., water inundation and small-scale debris flow) and subsequent consequential events (e.g., infrastructure failure and trapped citizens). It is imperative to achieve rapid identification of such events for timely and effective rainstorm disaster management. However, due to small-sample event classes, it becomes challenging to achieve the desired identification results. In this paper, a cross-domain transfer learning scheme is proposed to identify rainstorm events by combining citizen-report texts with multi-source spatial data. In the proposed framework, we recommend joint distribution adaptation (JDA) as the key to improving the identification of target events with inadequate samples by transferring knowledge from similar event domains. Meanwhile, multi-source spatial features are extracted and embedded in the text with linear discriminant analysis (LDA) to overcome the feature incompleteness that is caused by short text length. The proposed approach is verified based on the real data in Wuhan, China. Experimental results show that the knowledge transfer between different event classes helps cities to improve the performance of rainstorm event identification, with the accuracy increasing from 81% to 92%. Under data imbalance scenarios, the proposed scheme outperforms the state-of-the-art methods, such as sampling-based methods, ERNIE, and TCA. At the same time, the combination of textual and spatial features results in significant improvement in event identification, as evident by an 8% increase in accuracy. This framework can be followed to build a rainstorm event warning and assistance system to rapidly adapt management responses.