Upset Situations Research Articles

Prediction of esports competition outcomes using EEG data from expert players

Considerable efforts have focused on predicting sports event outcomes to enhance spectating, coaching, and betting. However, accurately predicting match results across various competitive scenes is challenging. Many researchers have achieved reasonable prediction accuracy using machine learning (ML) models and static data, such as player statistics up to the last match, to perform binary win/loss classification. However, the inherent uncertainty of sports often leads to unexpected outcomes. For example, narrowing the skill-level gap between players may negatively impact prediction accuracy. Moreover, accurately predicting “upsets,” where lower-skilled players defeat higher-skilled opponents, remains challenging. Conventional win/loss prediction techniques rely on static attributes derived from past match statistics, which fail to capture the player's condition immediately before the match. To address this limitation, this study focused on dynamic information reflecting the pre-match player's condition, particularly electroencephalography (EEG) data, recognized as a potent mental conditioning biomarker. We collected pre-match EEG data from esports experts and trained ML models on these data, aiming to assess the ability of ML techniques trained on EEG data to predict match outcomes, particularly in cases subject to high unpredictability. The findings revealed that the light gradient boosting machine (LightGBM) algorithm trained on EEG data achieved the highest prediction accuracy (80%), with parietal beta activity being the most relevant feature. Furthermore, predictive performance remained consistent even in match scenarios involving similar-level players and upset situations. Hence, this approach extends the applicability of win/loss prediction to traditionally unpredictable sports scenes, enhancing the quality of spectator experiences.

Modeling thermal analysis for predicting thermal hazards relevant to transportation safety and runaway reaction for 2,2′-azobis(isobutyronitrile)

The pure decomposition behavior of 2,2′-azobis (isobutyronitrile) (AIBN) and its physical phase transformation were examined and discussed. The thermal decomposition of this self-reactive azo compound was explored using differential scanning calorimetry (DSC) to elucidate the stages in the progress of this chemical reaction. DSC was used to predict the kinetic and process safety parameters, such as self-accelerating decomposition temperature (SADT), time to maximum reaction rate under adiabatic conditions (TMRad), and apparent activation energy (Ea), under isothermal and adiabatic conditions with thermal analysis models. Moreover, vent sizing package 2 (VSP2) was applied to examine the runaway reaction combined with simulation and experiments for thermal hazard assessment of AIBN. A thorough understanding of this reaction process can identify AIBN as a hazardous and vulnerable chemical during upset situations. The sublimation and melting of AIBN near its apparent onset decomposition temperature contributed to the initial steps of the reaction and explained the exothermic attributes of the peaks observed in the calorimetric investigation.

Process Resilience Analysis Framework (PRAF): A systems approach for improved risk and safety management

Risk management challenges and continuous increase of global public aversion to hazards and risks associated with the process industry have been observed in the recent years. In order to manage process industry risk, several studies and methods have been developed and are currently used. The authors believe that two types of interacting factors: 1) technical (equipment malfunction, process parameter variation), and 2) social (regulations/policy, human and organizational factors) are important in assessment of risk for a process system. However, current methods are based on analysis of either technical factors, often quantitatively, or social factors, usually qualitatively. Apart from failure to establish all critical scenarios due to either factors, their combined and interactive effects are seldom considered. This research need calls for the development of a holistic and integrated systems framework for effective risk management, although full coverage of possible mishaps will be utopian. The application of the resilience engineering perspective is gradually being explored as an approach for considering the dynamics of socio-technical aspects based on systems theory to provide a safety net. This paper presents a novel framework - Process Resilience Analysis Framework (PRAF) for incorporating both technical and social factors in an integrated approach. This is based on four aspects: Early Detection (ED), Error Tolerant Design (ETD), Plasticity (P) and Recoverability (R). The resilience methodology emphasizes dynamics, unforeseen and even unknown types of threats, uncertainty, systems degradation and complex interactions. With resilience metrics a combined framework for predictability, survivability and recoverability, all via dynamic analysis, is introduced. PRAF primarily focuses on early detection of unsafe domains of operation, assessment of aggregate risks and prioritization of safety barriers during process upset situations and reduction in response time resulting in a reduced frequency of loss of containment events (LoC), reduced consequences and enhanced recovery. The paper describes the concepts and principles of the PRAF as a first step.

Airplane loss of control problem: Linear controllability analysis

The controllability analysis for airplane flight dynamics is very crucial in upset/loss-of-control situations. Conventional airplanes are normally equipped with so redundant control authority. That is, an airplane might experience a loss of one or more control surfaces and remain controllable. As such, the aim of this paper is to investigate common upset situations and to explore the limits of controllability using linear analysis tools with emphasis on analysis of Thrust-only Flight Control Systems (TFCSs) where all the hydraulic systems are lost. Based on those analyses, the necessity of nonlinear controllability analysis for such situations is discussed.

Signal de-noising methods for fault diagnosis and troubleshooting at CANDU® stations

Over the past several years a number of domestic CANDU® stations have experienced issues with neutron detection systems that challenged safety and operation. Intelligent troubleshooting methodology is required to aid in making risk-informed decisions related to design and operational activities, which can aid current stations and be used for the future generation of CANDU® designs. Fault modelling approach using Fault Semantic Network (FSN) with risk estimation is proposed for this purpose. One major challenge in troubleshooting is the determination of accurate data. It is typical to have missing, incomplete or corrupted data points in large process data sets from dynamically changing systems. Therefore, it is expected that quality of obtained data will have a direct impact on the system's ability to recognize developing trends in the process upset situations. In order to enable fault detection process, intelligent filtering techniques are required to de-noise process data and extract valuable signal features in the presence of background noise. In this study, the impact of applying an optimized and intelligent filtering of process signals prior to data analysis is discussed. This is particularly important for neutronic signals in order to increase signal-to-noise ratio (SNR) which suffers the most during start-ups and low power operation. This work is complimentary to the previously published studies on FSN-based fault modelling in CANDU stations. The main objective of this work is to explore the potential research methods using a specific case study and, based on the results and outcomes from this work, to note the possible future improvements and innovation areas.

Thermal hazard accident investigation of hydrogen peroxide mixing with propanone employing calorimetric approaches

Hydrogen peroxide (H 2O 2), historically, due to its broad applications in the chemical industries, has caused many serious fires and explosions around the world. Its thermal hazards may also be incurred by an incompatible reaction with other chemicals, and a runaway reaction may be induced in the last stage. This study applied thermal analytical methods to explore the H 2O 2 leading to these accidents by incompatibility and to discuss what might be formed by the upset situations. Thermal hazard analysis contained a solvent, propanone (CH 3COCH 3, so-called acetone), which was deliberately selected to mix with H 2O 2 for investigating the degree of thermal hazard. Differential scanning calorimetry (DSC) and vent sizing package 2 (VSP2) were employed to evaluate the thermal hazard of H 2O 2. The results indicated that H 2O 2 is highly hazardous while mixed with propanone, as a potential contaminant. The time to maximum rate (TMR) was used as emergency response time in the chemical industries. Therefore, TMR of H 2O 2 was calculated to be 70 min for runaway reaction (after T 0) and TMR of H 2O 2/propanone was discovered to be 27 min only. Fire and explosion hazards could be successfully lessened if the safety-related data are properly imbedded into manufacturing processes.

Thermal hazard analyses and incompatible reaction evaluation of hydrogen peroxide by DSC

Hydrogen peroxide (H2O2), historically, due to its broad applications in the chemical industries, has caused many serious fires and explosions worldwide. Its thermal hazards may also be incurred by an incompatible reaction with other chemical materials, and a runaway reaction may be induced in the last stage. This study applied thermal analytical methods to explore the H2O2 leading to thermal accidents by incompatibility and to discuss what might be formed by the upset situations. In this study, the thermal hazard analyses were conducted with various solvents, propanone (CH3COCH3), Fe2O3, FeSO4, H2SO4, HCl, HNO3, H3PO4, NaOH, LiOH, and KOH which were deliberately selected to individually mix with H2O2 for investigating the degree of hazard. Differential scanning calorimetry (DSC) was employed to evaluate the thermal hazard of H2O2-mixed ten chemicals. The results indicated that H2O2 is highly hazardous while separately mixed with ten materials, as a potential contaminant. Fire and explosion hazards could be successfully reduced if the safety-related data are suitably imbedded into manufacturing processes.

Effects of acetone on methyl ethyl ketone peroxide runaway reaction

Runaway reactions by methyl ethyl ketone peroxide (MEKPO) are an important issue in Asia, due to its unstable structure and extensive heat release during upset situations. This study employed differential scanning calorimetry (DSC) to draw the experimental data for MEKPO 31 mass% and with acetone 99 mass% on three types of heating rate of 2, 4, and 10 °C/min; the kinetic and safety parameters were then evaluated via curve fitting. Through the reproducible tests in each condition, the results show that acetone is not a contaminant, because it could increase the activation energy ( E a) and onset temperature ( T o) when combined with MEKPO, which differs from the hazard information of the material safety data sheet (MSDS).

Dynamic Modeling of a Combined-Cycle Plant

Greater use is being made of dynamic simulation of energy systems as a design tool for selecting control strategies and establishing operating procedures. This paper discusses the dynamic modeling of a gas-fired combined-cycle power plant with a gas turbine, a steam turbine, and an alternator—all rotating on a common shaft. A waste-heat boiler produces steam at two pressures using heat from the gas turbine flue gas. The transient behavior of the system predicted by the model for various upset situations appears physically reasonable and satisfactory for the operating constraints.

Optimization of resistively hardened latches

The design of digital circuits tolerant to single-event upsets is considered. The results of a study in which an analytical model was used to predict the behavior of a standard resistively hardened latch are presented. It is shown that a worst-case analysis for all possible single-event upset situations (on the latch or in the logic) can be derived from studying the effects of a transient distributed write cycle. The existence of an intrinsic minimum write period to tolerate a transient of a given duration is also demonstrated. This minimum write period cannot be attained without proper resistor selection resulting from a complete optimization study. Analytic results are in sufficiently good agreement with SPICE results to guide simulation choices efficiently, and the model made it possible to develop a set of linear equations that allow the quick optimization of the studied latch for any transient durations and any IC CMOS processes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Relapse crises and coping among dieters.

We examined situational antecedents of dieting relapse crises and dieters' attempts to cope with temptations to overeat. We analyzed posttreatment interviews with 57 obese Ss with Type II diabetes, comparing situations in which Ss lapsed with those in which they overcame temptation to overeat. Cluster analysis yielded 3 categories of relapse crises: mealtime, low-arousal, and emotional upset situations. The clusters differed in outcome: Upset situations almost always resulted in overeating; situational factors, especially food-related cues, increased relapse risk; but performance of coping was the strongest correlate of outcome. Cognitive and behavioral coping responses were each equally associated with positive outcomes. When Ss reported combining both types of coping, they were less likely to report overeating. The dynamics of relapse crises among dieters resemble those that govern relapse crises in addictive behaviors.

A cluster-analytic classification of smoking relapse episodes

Using reports of relapse crises collected through a relapse prevention hotline, this paper develops a taxonomy of relapse situations. Cluster analysis resulted in four clusters. Social situations are characterized by social drinking and exposure to smoking. Relaxation situations are marked by relaxation, usually after a meal. Work situations take place during work when the exsmoker is feeling anxious. Upset situations occur when the exsmoker is feeling anxious or depressed while home alone. These situations were significantly discriminated by their scores on the McKennel Smoking Motivation Scales, suggesting a connection between smoking patterns and relapse. Behavioral coping was less likely and relapse more likely in social and upset situations. The frequency of coping accounted for the higher relapse rates in Upset, but not in social situations. Relapse was nearly inevitable among people who failed to cope behaviorally in social situations, suggesting that they are especially tempting.