Defence in depth is a time-tested strategy in some technical fields, such as cybersecurity or nuclear reactor technology. In view of potentially devasting consequences of exponential growth, the implementation of such a strategy is almost self-evident in this case. Less obvious is a formal explanation of why and when such a control strategy is necessary compared to traditional risk management approaches and which systems or organisations can benefit from it. Quantitative arguments are given based on the properties of heavy tail stochastic variables. Particularly, their single big-jump property provides the rationale to require a hierarchy of barriers for containment of a cascade of daughter events. The COVID-19 pandemic has vividly demonstrated how difficult it is to implement an appropriate control strategy under these circumstances. Concretely, it is shown that the spread of transport vectors in this case has heavy tail properties and consequently requires a defence in depth response.
Uncertainty must be considered when developing business strategies as part of a corporate strategic management process, to ensure that companies have adequate resilience to withstand crises. The concept of a 'robust company' outlined in strategic management terms in this paper is an integrative approach that combines findings from the areas of risk management, strategic management, corporate ratings, and insolvency research, as well as empirical capital market research. Robustness is the ability of a system to survive negative shocks while maintaining a defined minimum level of success (performance) in the long term. Research findings suggest that robust companies exhibit the following three key characteristics: high financial sustainability (a stable rating, low earnings risk), a robust strategy with stable strategic success potential as a driver of future financial performance and company value, and a high level of competence in dealing with risk, especially in making business decisions.
The paper demonstrates a new domain-independent method for improving reliability and reducing risk including two fundamental approaches: the forward approach, based on deriving algebraic inequalities from real systems and processes; and the inverse approach, based on deriving new knowledge by meaningful interpretation of existing correct algebraic inequalities. The forward approach has been used to prove the domain-independent principle of the well-ordered systems which are characterised by the smallest possible risk of failure. The inverse approach has been used to generate new knowledge related to the relationship of the equivalent elastic constants of elements arranged in series and parallel and the upper and lower bounds of the percentage of faulty components in pooled batches of components with unknown sizes.
A unified fuzzy system has been developed in this paper for assessing weighted postural risk score (PRS) of body parts by reducing errors relating to human perception in measuring exact body joint angles corresponding to different working postures used as inputs of rapid upper limb assessment (RULA) process. In the proposed method a Mamdani fuzzy inference system is generated using two input parameters based on modified Nordic questionnaire and RULA. Fuzzy analytic hierarchy process is incorporated to calculate the weight of postural risk associated with each body part using the body part discomfort scale. Finally, weighted PRS of each body part is evaluated to identify the discomfort prone body parts for taking preventive strategies well in advance. To establish application potentiality of the proposed methodology a case study is performed for assessing weighted PRS of different discomfort prone body parts of female brick moulders engaged in several brick fields.
The present paper inquires the realisation of activities within the occurrence of an event with an active shooter. The purpose of the paper is to explore the range to which Slovenian members of the Emergency medical treatment (EMT) prehospital teams were familiar to the action recommendations of EMT teams in the occurrence of an AMOK situation. Further purpose was to inquire whether EMT teams are ready to confirm the link between awareness and their perceived competences. An online survey among 252 members of the EMT prehospital teams was employed, using a convenience sampling. The analysis shows that EMT team members agree that the regularity of joint drill with armed forces importantly affects their qualification for a correct action. But nevertheless, EMT members seem to be well-informed about the recommendations from a theoretical point of view. This study makes several contributions to the EMT teams' assessment of their readiness to respond in the case of an AMOK attack in a relatively safe European country.
Data analysis can be done by expert system decisions on system status according to system input and output data. For the purpose of data analysis, there is often a need to classify data or to find regularities therein. The results of the regularity search can be expressed by the IF-THEN production rules. The use of different approaches - with clustering algorithms, neural networks - makes it possible to obtain rules that characterise data. Knowledge acquisition in this paper is the process of extracting knowledge from numerical ata in form of rules. Rules acquisition in this context is based on clustering methods. With the help of the K-means clustering algorithm, rules are derived from trained neural networks. The rule-making methodology is demonstrated on a sample basis of IRIS data. The effectiveness of the obtained rules is evaluated.
The scientific problem "Event-driven management of the quality of economics and state from 'below'" is formulated based on artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, education, decision-making. Events in these processes and the corresponding logical variables relate to the behaviour of humans, others, and infrastructure. The processes of a person's quality of life are modelled, analysed and controlled with the participation of the person himself. The paper illustrates this problem only by the examples of one government, one economics. Scenarios and structural, logical and probabilistic models for managing the quality of human life are presented. The relationship between the management of the quality of human life and the digital economics is considered.
The debates over uncertainty about the possible undesirable impacts on human and the environmental safety of GM foods have increased.Most of these debates are rooted in different risk perceptions of various societal stakeholders.Despite growing studies on GM foods' knowledge, attitude, or behaviour, few have documented the risk perception models to assess stakeholders' risk perceptions.Therefore, this study aimed to develop a model to explain the social risk perception of GM foods.To this end, current risk perception theories and models were critically reviewed, then an attempt was made to develop an integrated and more comprehensive model called the "Comprehensive Social Risk Perception Model" (CSRPM).Addressing the theoretical and methodological weakness of conventional models, CSRPM could be a complementary model to more effectively study the social risk perception of GM foods in developed and developing countries.Furthermore, CSRPM can also be used in ex-ante and ex-post risk assessments.
