ABSTRACT
The academy form competences to work in the routine, without worries, without unknown hazards. The rules and objectives in routine activities are clear and require operational discipline to control the cost of production and product quality. The current educational model does not prepare society and groups for an emergency or even for decisions in a crisis, where resources are scarce mainly because the necessary knowledge about danger and its mechanism of action is lacking. What really happens in the chain event scenario when the danger is unknown? What are the typical models of action of the emergency leader and his team? What are the required actions in crisis management required for a BS type event considering the scarce resources, the high level of stress and the direction of the danger energy? Which models are indicated for the situation of unknown danger and its intense energy flow with high impacts during these events? What is the appropriate mind map of those who lead and execute the actions considering the low level of visibility in the events and the dynamic of geopolitics in the BS scenario where, depending on the preparation for emergency, in case of intense demand for attention and action, possible modifications may occur in the map mental for decisions? This work intends to review the discussions already performed about new concepts and tools to be used in the crisis prevention area. Between this works we will understand the effect of the stress level on emergency decisions valuating human error and observing team response using LODA tool. The Human elements designed in the organizational culture, can stop the high danger energy that can flow from some industries. The principal aspects to be constructed and monitored are technology (risk and complexity);management (leadership and stress);and behavior (cooperation, commitment, competence, and communication). By the other hand, human factors are analyzed to avoid situations which hazard energy carriers during top events, as the effect of cultural aspects to chain reaction until the occurrence of disaster. Decision models for the emergency brigade (leader and team) indicate the motor and cognitive gaps that cause lack of control during contingencies. Finally, the analysis of simulation types, by rotating observation of field simulation allow find the deficiencies of the emergency team in a real situation in the future. The methodology divided in following steps are dynamic: first, rescue the discussions about stress and the BS events and, in second step, recognize the intense hazard energy flow through failure events, antecedents, elements and human factors. In third step, we try to locate the gaps and environmental conflicts influencing losses from accidents. Finally, we assess the current event of covid-19 pandemics to suggest correct emergency preparedness actions in the preparation of leaders and their teams regarding cognitive and motor characteristics in situations of high demand. When comparing the characteristics already described by TALEB about BS with the event COVID-19, we intend to carry out a case study to indicate the possible causes this global impact crisis, or even, understand the technological disaster causes where uncontrolled events from chemical, nuclear, and oil industry begins chain reactions until the disaster. Unfortunately, a Crisis situation can be caused from several intricated dimensions, making difficult the contingency and mitigation actions. © 36th Center for Chemical Process Safety International Conference, CCPS 2021 - Topical Conference at the 2021 AIChE Spring Meeting and 17th Global Congress on Process Safety.
ABSTRACT
The academy form competences to work in the routine, without worries, without unknown hazards. The rules and objectives in routine activities are clear and require operational discipline to control the cost of production and product quality. The current educational model does not prepare society and groups for an emergency or even for decisions in a crisis, where resources are scarce mainly because the necessary knowledge about danger and its mechanism of action is lacking. What really happens in the chain event scenario when the danger is unknown? What are the typical models of action of the emergency leader and his team? What are the required actions in crisis management required for a BS type event considering the scarce resources, the high level of stress and the direction of the danger energy? Which models are indicated for the situation of unknown danger and its intense energy flow with high impacts during these events? What is the appropriate mind map of those who lead and execute the actions considering the low level of visibility in the events and the dynamic of geopolitics in the BS scenario where, depending on the preparation for emergency, in case of intense demand for attention and action, possible modifications may occur in the map mental for decisions? This work intends to review the discussions already performed about new concepts and tools to be used in the crisis prevention area. Between this works we will understand the effect of the stress level on emergency decisions valuating human error and observing team response using LODA tool. The Human elements designed in the organizational culture, can stop the high danger energy that can flow from some industries. The principal aspects to be constructed and monitored are technology (risk and complexity);management (leadership and stress);and behavior (cooperation, commitment, competence, and communication). By the other hand, human factors are analyzed to avoid situations which hazard energy carriers during top events, as the effect of cultural aspects to chain reaction until the occurrence of disaster. Decision models for the emergency brigade (leader and team) indicate the motor and cognitive gaps that cause lack of control during contingencies. Finally, the analysis of simulation types, by rotating observation of field simulation allow find the deficiencies of the emergency team in a real situation in the future. The methodology divided in following steps are dynamic: first, rescue the discussions about stress and the BS events and, in second step, recognize the intense hazard energy flow through failure events, antecedents, elements and human factors. In third step, we try to locate the gaps and environmental conflicts influencing losses from accidents. Finally, we assess the current event of covid-19 pandemics to suggest correct emergency preparedness actions in the preparation of leaders and their teams regarding cognitive and motor characteristics in situations of high demand. When comparing the characteristics already described by TALEB about BS with the event COVID-19, we intend to carry out a case study to indicate the possible causes this global impact crisis, or even, understand the technological disaster causes where uncontrolled events from chemical, nuclear, and oil industry begins chain reactions until the disaster. Unfortunately, a Crisis situation can be caused from several intricated dimensions, making difficult the contingency and mitigation actions. © 2021 American Institute of Chemical Engineers. All rights reserved.
ABSTRACT
Digital forensics readiness (DFR) is an important part of the growing forensic domain. Research on DFR has been given little attention, while available DFR models have focused on theoretical investigations with inadequate input from practicing information security experts in the industry. Using feedback from practicing forensic experts in the industry and academia, this research investigates the structure required to implement and manage digital forensic readiness (DFR) within an enterprise. The research extended the DFR Commonalities framework (DFRCF) and utilised the structure to design a digital forensic maturity assessment model (DFMM) that will enable organisations to assess their forensic readiness and security incident responses. A combination of qualitative and research design approaches was utilised to perform a comparative analysis of various DFR frameworks. A top-down design approach was utilised in developing the DFMM model which was validated with forensic practitioners and academics through semi-structured interviews. The structure extracted from DFR frameworks was practical since most participants agreed with the structure of the extended DFRCF and the matrix of the maturity model. Overall, key changes were introduced to enhance both the extended DFRCF and the DFMM. The study was limited to participants who have a forensic footprint and are knowledgeable about DFR. This paper thereby provides practitioners, academics and organisations with access to a non-propriety DFMM maturity model.
ABSTRACT
BACKGROUND: The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. OBJECTIVE: This study aims to inform the feasibility of leveraging broad, statewide datasets for population health-driven decision-making by developing robust analytical models that predict COVID-19-related health care resource utilization across patients served by Indiana's statewide Health Information Exchange. METHODS: We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). RESULTS: For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). CONCLUSIONS: This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.