PFL: A Probabilistic Logic for Fault Trees

Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PFL: A Probabilistic Logic for Fault Trees

Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Three-Way Group Decision Making Based on Evidential Reasoning with Probabilistic Linguistic Term Assessments

In consideration of the different importance degrees that may be assigned to all possible linguistic terms, this paper investigates a novel three-way group decision-making method based on the probabilistic linguistic term set (PLTS) information systems. We first construct PLTS information systems based on multiple attributes. Considering the reliabilities of the experts, we determine the weights of the experts by the similarities of the information provided by the expert with regard to other experts. Subsequently, using the evidential reasoning (ER) method, we aggregate the information provided by all experts and obtain the conditional probability of each object. The introduction of the ER rules and the weights of experts successfully solve the problem of conflict between the evaluation information. Then an approach is presented to calculate loss functions and thresholds, which reduces the subjectivity of the decision-making process. Next, the decision result of each object is deduced based on the minimum-loss principle. Finally, a case study about the selection of mask foundries during the COVID-19 is used to demonstrate the effectiveness of our proposed method. And the superiority of our proposed method are proved by comparative analysis. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

Research on Data Analysis and Visualization of Recruitment Positions Based on Text Mining

With the rapid development of the Internet and the impact of COVID-19, online recruitment has gradually become the mainstream form of recruitment. However, existing online recruitment platforms fail to fully combine the job seekers' demands for salary, region, benefits, and other aspects, which cloud not display the information related to recruitment positions in a multidimensional way. To solve this problem, this paper firstly uses a web crawler to collect job information from recruitment websites based on keywords retrieved by users, then extracts job information using regular expressions, and cleans and processes the extracted job information using third-party libraries such as Pandas and NumPy. Finally, through the probabilistic theme model of text mining, the topic model of job description content in the recruitment information is modeled. Combining with the django development framework and related visualization technology, the relationship among education requirement, experience requirement, job location, salary, and other aspects in the recruitment information is visually displayed in a multidimensional way. At the same time, the GM model is used to realize the gray prediction of the number of employment personnel in related industries, which provides employment reference for the majority of job seekers and enterprises. © 2022 Yuanyuan Chen and Ruijie Pan.

A regret theory-based GEDM method with heterogeneous probabilistic hesitant information and its application to emergency plans selection of COVID-19

Emergency events are happening with increasing frequency, inflicting serious damage on the economic development and human life. A reliable and effective emergency decision making method is great for reducing various potential losses. Hence, group emergency decision making (GEDM) has drawn great attention in past few years because of its advantages dealing with the emergencies. Due to the timeliness and complexity of GEDM, vagueness and regret aversion are common among decision makers (DMs), and decision information usually needs to be expressed by various mathematical forms. To this end, this paper proposes a novel GEDM method based on heterogeneous probabilistic hesitant information sets (PHISs) and regret theory (RT). Firstly, the PHISs with real numbers, interval numbers and linguistic terms are developed to depict the situation that decision group sways precariously between several projects and best retain the original assessment. In addition, the score functions, the divergence functions and some operations of the three types of PHISs are defined. Secondly, the normalization model of PHISs is presented to remove the influence of different dimensions on information aggregation. Thirdly, group satisfaction degree (GSD) based on the score functions and the divergence functions is combined with RT for completely portraying the regret perception of decision group. Then, we introduce Dempster-Shafer (DS) theory to determine the probabilities of future possible states for emergency events. Finally, an example of coronavirus disease 2019 (COVID-19) situation is given as an application for the proposed GEDM method, whose superiority, stability and validity are demonstrated by employing the comparative analysis and sensitivity analysis. © 2022 - IOS Press. All rights reserved.

Emergency Decision Model by Combining Preference Relations with Trapezoidal Pythagorean Fuzzy Probabilistic Linguistic Priority Weighted Averaging PROMETHEE Approach

The outbreak of COVID-19 in 2019 has brought greater international attention to emergency decision making and management. Since emergency situations are often uncertain, prevention and control are crucial. For better prevent and control, according to the characteristics of emergency incidents, the paper proposes a new form of linguistic expression trapezoidal Pythagorean fuzzy probabilistic linguistic variables to express decision-making information. Next, the paper develops the operational rules, value index and ambiguity of trapezoidal Pythagorean fuzzy probabilistic linguistic variables. Then, the new trapezoidal Pythagorean fuzzy probabilistic linguistic priority weighted averaging PROMETHEE approach is introduced to aggregate the trapezoidal Pythagorean fuzzy probabilistic linguistic information combining with preference relation. Finally, an emergency decision making case of prevention of infectious diseases analysis illustrate the necessity and effectiveness of this method, the results of comparative and experimental analyses demonstrate that the constructed new trapezoidal Pythagorean fuzzy probabilistic linguistic priority weighted averaging PROMETHEE approach owns better performances in terms of effectiveness and reasonability. © 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved.

An unsupervised framework for tracing textual sources of moral change

Morality plays an important role in social wellbeing, but people's moral perception is not stable and changes over time. Recent advances in natural language processing have shown that text is an effective medium for informing moral change, but no attempt has been made to quantify the origins of these changes. We present a novel unsupervised framework for tracing textual sources of moral change toward entities through time. We characterize moral change with probabilistic topical distributions and infer the source text that exerts prominent influence on the moral time course. We evaluate our framework on a diverse set of data ranging from social media to news articles. We show that our framework not only captures fine-grained human moral judgments, but also identifies coherent source topics of moral change triggered by historical events. We apply our methodology to analyze the news in the COVID-19 pandemic and demonstrate its utility in identifying sources of moral change in high-impact and real-time social events. © 2021 Association for Computational Linguistics.

Geometrical Schemes as Probabilistic and Entropic Tools to Estimate Duration and Peaks of Pandemic Waves

At the end of first quarter of 2020 it was seen in most countries statistics the beginning of an imminent second wave of pandemic. On January of 2021 it was seen in the data a rapid growth of new infections. In this paper, a geometry-based scheme is presented. In concrete the rectangle and trapezoid shapes are analyzed. From this, a relation between both geometries is extracted in terms of polynomial functions. The resulting characterization of a pandemic in terms of geometric variables is presented. Thus the present model is confronted with official data of USA and India. From the results of this paper, it is strongly believed that entropy might be behind of a global pandemic dynamics. © 2021 IEEE.

The Conjunction of Deterministic and Probabilistic Events in Realistic Scenarios of Outdoor Infections

The aim of this paper is the derivation of an robust formalism that calculates the so-called social distancing as already determined in the ongoing Corona Virus Disease 2019 (Covid-19 in short) being established in various places in the world between 1.5 m and 2.5 m. This would constitutes a critic space of separation among people in the which aerosols might not be effective to infect healthy people. In addition to wearing masks and face protection, the social distancing appears to be critic to keep people far of infections and consequences produced from it. In this way, the paper has opted by the incorporation of a full deterministic model inside the equation of Weiss, by the which it fits well to the action of outdoor infection when wind manages the direction and displacement of aerosols in space. Thus, while a deterministic approach targets to propose a risk’s probability, a probabilistic scenario established by Weiss in conjunction to the deterministic events would yield an approximated model of outdoor infection when there is a continuous source of infected aerosols that are moving through air in according to a wind velocity. The simulations have shown that the present approach is valid to some extent in the sense that only the 1D case is considered. The model can be extended with the implementation of physical variables that can attenuate the presence of disturbs and random noise that minimizes the effectiveness of present proposal. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Optimal nursing home service scheduling under Covid-19 related probabilistic staff shortage: A two-stage stochastic programming approach

Maintaining an appropriate staffing level is essential to providing a healthy workplace environment at nursing homes and ensuring quality care among residents. With the widespread Covid-19 pandemic, staff absenteeism frequently occurs due to mandatory quarantine and providing care to their inflicted family members. Even though some of the staff show up for work, they may have to perform additional pandemic-related protection duties. In combination, these changes lead to an uncertain reduction in the quantity of care each staff member able to provide in a future shift. To alleviate the staff shortage concern and maintain the necessary care quantity, we study the optimal shift scheduling problem for a skilled nursing facility under probabilistic staff shortage in the presence of pandemic-related service provision disruptions. We apply a two-stage stochastic programming approach to our study. Our objective is to assign staff (i.e., certified nursing aids) to shifts to minimize the total staffing cost associated with contract staff workload, the adjusted workload for the changing resident demand, and extra workload due to required sanitization. Thus, the uncertainties considered arise from probabilistic staff shortage in addition to resident service need fluctuation. We model the former source of uncertainty with a geometric random variable for each staffer. In a proof-of-the-concept study, we consider realistic COVID-19 pandemic response measures recommended by the Indiana state government. We extract payment parameter estimates from the COVID-19 Nursing Home Dataset publicly available by the Centers for Medicare and Medicaid Services (CMS). We conclude with our numerical experiments that when a skilled nursing facility is at low risk of the pandemic, the absenteeism rate and staff workload increase slightly, thus maintaining the current staffing level can still handle the service disruptions. On the other hand, under high-risk circumstances, with the sharp increase of the absence rate and workload, a care facility likely needs to hire additional full-time staff as soon as possible. Our research offers insights into staff shift scheduling in the face of uncertain staff shortages and service disruption due to pandemics and prolonged disasters. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

Investigative and probabilistic perspective of the accidental release of styrene: a case study in Vizag, India

In addition to revealing the cracks in global health care, emergency preparedness, and response systems, COVID-19 also exposed the lack of capacity to run chemical plants safely under such conditions. On 7th May 2020, self-polymerization runaway reaction from an atmospheric storage tank in a polymer facility triggered the release of styrene to the atmosphere, resulting in 12 fatalities and hospitalization of more than 1000 individuals. A similar incident had happened 35 years back at Bhopal involving the release of methyl isocyanate resulting in one of the deadliest process safety incidents to date. Therefore, it is very important to understand the causal factors so that such high-risk incidents can be prevented in future. This paper presents a comprehensive investigative study of styrene gas leak at Vizag with special emphasis on probabilistic risk analysis of the loss of containment. Hazard perception study was performed to understand the possible hazardous scenarios in bulk styrene storage facilities for preventing such catastrophic recurrences. Energy barrier analysis was performed to study the inadequacy of pro-active and reactive barriers with respect to the accident case study. The analysis also considers the escalation factors resulting from extremities of COVID-19 lockdown. The self-polymerization reaction that resulted in toxic styrene dispersion was preventable owing to the advancements in safety engineering and loss prevention since Bhopal Gas Tragedy (1984). Based on the investigative analysis, it can be pointed out that this accident would have occurred even in the absence of COVID-19 restrictions, mainly due to negligence and complacency shown towards safety by the company's management. © 2021 Institution of Chemical Engineers