Demo Abstract: Implementing a COVID-19 Risk Assessment System for User-Informed Travel Planning in Harris County

The COVID-19 pandemic has forced people worldwide to modify their daily activities, including travel plans. To help individuals make informed decisions about visiting public places, Cheng [2] first proposed a real-time COVID-19 risk assessment system called RT-CIRAM and implemented prototypes for two U.S. metropolitan locations. The system calculates a COVID-19 risk score and categorizes the risk levels into high, medium, and low, recommends the safe travel destination using the users' location and the specified distance the user is willing to travel, thereby helping users make informed decisions about their travel plans. © 2023 ACM.

Near-field exposure of pathogen-laden respiratory particles based on statistical evaluation of one emitting person indoors

Risk assessment models typically assume ideal mixing, in which the pathogen-laden aerosol particles emitted by a person are evenly distributed in the room. This study points out the local deviation from this idealized assumption and a correlation between the level of pathogen concentration and the distance from the emitter. For this purpose, several numerical studies (CFD) were analyzed, and a validation experiment was performed. Statistical evaluation of the spatial pathogen distribution was used to determine the potential exposure to elevated pathogen concentrations. Compared to an ideally mixed room, at a distance of 1.5 m, the mixing ventilation cases show a 25% risk of being exposed to twice the amount of pathogens and a 5% risk to more than 5 times the assumed value. For displacement ventilation there is a 75% chance of being exposed to less pathogens than in complete mixing at a distance of 1 m. The measurement values agree with the simulation results. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Understanding occupancy pattern of university libraries in the post-pandemic era

Understanding indoor occupancy patterns is crucial for energy model calibration, efficient operations of fresh air systems, and COVID-19 exposure risk assessment. University libraries, as one of centers of campus life, due to the high mobility and "foot-voting” nature of them, i.e., occupants pick seats in the micro-environments they prefer, provide a non-intrusive opportunity to carry out post-occupancy evaluations. We conducted a long-term online monitoring of occupancy in libraries of a university in China by web-crawling the online seat reservation system, based on which, we constructed two sets of databases consisting of around 70 million records of nearly 3, 000 seats in 4 library sections, with seat-level resolution and sampling frequency up to every 10 seconds. The informative data set depicts not only the overall spatio-temporal occupancy patterns, but also nuances hidden within seats and visits. The daily flow of the main libraries exceeded two visits per seat. Half of the visitors stayed at the libraries for 3-6 hours during a single occupancy. Semester schedules and campus accessibility together influence students' decisions on when and which library to go, while even within the same zone, some seats were always more popular than their neighbours. "Semi-isolation” is one of the candidate attractive features proposed to understand the underlying patterns. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

SARS-CoV-2 risk assessment for a 30-minute car journey

In this paper a numerical methodology for close proximity exposure (<2m) is applied to the analysis of aerosol airborne dispersion and SARS-CoV-2 potential infection risk during short journeys in passenger cars. It consists of a three-dimensional transient Eulerian-Lagrangian numerical model coupled with a recently proposed SARS-CoV-2 emission approach, using the open-source software OpenFOAM. The numerical tool, validated by Particle Image Velocimetry (PIV), is applied to the simulation of aerosol droplets emitted by a contagious subject in a car cabin during a 30-minute journey and to the integrated risk assessment for SARS-CoV-2 for the other passengers. The effects of different geometrical and thermo-fluid-dynamic influence parameters are investigated, showing that both the position of the infected subject and the ventilation system design affect the amount of virus inhaled and the highest-risk position inside the passenger compartment. Calculated infection risk, for susceptible passengers in the car, can reach values up to 59%. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Need of Technological Interventions for Indoor Air Quality and Risk Assessment Upon Short-Term Exposure: A Futuristic Approach

The variation in ambient air pollution hampers indoor air quality (IAQ), and even the short-term variation is very hazardous for the exposed population. Technological interventions including sensors, smartphones and other gadgets are implemented to build smart environments. However, these interventions are still not fully explored in developing countries like India. The COVID-19 pandemic has made it very important to keep a tab on the air we breathe in as those already suffering from respiratory troubles are prone to fall victim to the deadly disease. In such a scenario, even a rise in pollution for a short duration is dangerous to the exposed pollution. Such short-term exposure facilitated by the meteorological creates a disaster for environmental health. The short-term rise in the concentration of pollutants makes things worse for the exposed people, even indoors. It is therefore critical to come up with a concrete solution to predict the IAQ instantly and warn the exposed population which can be only achieved by technological interventions and futuristic Internet of Things-based computational predictions. This chapter is intended to elaborate the health hazards linked to short-term rise in pollutants, which often goes unnoticed but has a critical impact and how with the help of IoT-based applications, the short-term variation can be predicted through different strategies. Similarly, the assessment of the health impact associated with short-term exposure to air pollution is also significant, and different exposure assessment models and computational strategies are discussed in the course of the study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

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.

Research on Financial Systemic Risk Assessment Algorithm Based on Risk Data Fuzzy Cluster Analysis

The financial industry is a high-risk industry. Once the financial industry risk happen, it will affect the economic development. Ensuring the safe, efficient and steady operation of finance and preventing systemic financial risks are the urgent needs of China's opening up to the outside world and building a well-off society in an all-round way. Stable and efficient economic development is the basis of financial risk prevention and control, which is the inherent requirement of high-quality economic development. Strengthening macro-prudential management has become the core content of financial regulatory reform in major international organizations and economies after the international coronavirus outbreak and preventing systemic financial risks is the fundamental goal of macro-prudential management. This paper takes the assessment and monitoring of China's systemic financial risks as the research object, and proposes an assessment algorithm of systemic financial risks based on risk data fuzzy clustering analysis. The established financial systemic risk measurement method can identify risks to a certain extent, deeply understand the nature, root and key areas of systemic financial risks, and build a long-term mechanism to prevent and resolve systemic financial risks. © 2022 IEEE.

Assessment of respiratory disease infection risk and natural ventilation intervention countermeasures in teaching spaces: A campus case study

The risk of indoor respiratory disease transmission can be significantly reduced through interventions that target the built environment. Several studies have successfully developed theoretical models to calculate the effects of built environment parameters on infection rates. However, current studies have mainly focused on calculating infection rate values and comparing pre- and post-optimization values, lacking a discussion of safe baseline values for infection rates with risk class classification. The purpose of this paper is to explore the design of interventions in the built environment to improve the ability of buildings to prevent virus transmission, with a university campus as an example. The study integrates the Wells-Riley model and basic reproduction number to identify teaching spaces with high infection risk on campus and proposes targeted intervention countermeasures based on the analysis of critical parameters. The results showed that teaching buildings with a grid layout pattern had a higher potential risk of infection under natural ventilation. By a diversity of building environment interventions designed, the internal airflow field of classrooms can be effectively organized, and the indoor virus concentration can be reduced. We can find that after optimizing the building mentioned above and environment intervention countermeasures, the maximum indoor virus infection probability can be reduced by 22.88%, and the basic reproduction number can be reduced by 25.98%, finally reaching a safe level of less than 1.0. In this paper, we support university campuses' respiratory disease prevention and control programs by constructing theoretical models and developing parametric platforms. © 2023 Elsevier Ltd

Risk assessment using information entropy

Risk assessment, particularly when using simulations, requires that the analyst develops estimates of expected, low, and high values for inputs. Mean and standard deviation are often used to assess the variability of metrics, assuming that the underlying distribution is normal. However, it is increasingly realized that non-normal distributions are common and important. If data are available, it is simple and straightforward to check this assumption by computing higher order moments.Claude Shannon [1], [2] proposed that the information entropy for a set of N discrete events can be measured by (Formula Presented) E. T. Jaynes [3] proposed that, if data is available, information entropy can be maximized using Lagrangian multipliers and that the resulting probability distribution maximizes the uncertainty of that distribution given the data.In order to use entropy maximization, it is required to define constraints such that Σpi = 1, plus constraints on the mean, variance, skew, kurtosis, and other moments. This problem does not have a closed form solution but can be solved iteratively in a spreadsheet.The problem can be set up as follows for mean bar x and variance s2: (Formula Presented) This basic formulation models the normal distribution. The importance of non-normality can be estimated by adding higher order moments as desired. For n ≥ 3, constraints can be added using: (Formula Presented) where Mn is the computed nth moment of the data set.Differentiating ∂H/∂pi = 0 maximizes information entropy, and the resulting probability distribution has the most uncertainty given the observed data.This suggests that it is possible to develop an estimate of the distribution where some values are underrepresented in the sample. It further suggests that unusual or atypical results can be better estimated.This paper uses the method of maximizing entropy to model observed data and will study two time series applications. One problem of interest is sequential acquisition of data. For example, time to failure for a device may be a metric of concern. A maximum entropy model provides an empirical estimate of the distribution of this metric. A second problem of interest is forecasting the distribution of a metric at some point in the future. This applies to supply chain management. Project sponsors prepare cost and schedule estimates well in advance of placing the orders for the materials used in those projects. Management reserves for cost and schedule are typically set by subject matter experts, and recent experience (e.g., supply chain disruptions due to the COVID19 pandemic) may overemphasize current data when developing risk assessments. This approach offers a datadriven way to empirically develop risk assessments. © 2023 IEEE.

Towards Predicting Supplier Resilience: A Tree-Based Model Approach

With looming uncertainties and disruptions in today's global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions. © 2022 IEEE Computer Society. All rights reserved.

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.

RISK ASSESSMENT METHOD OF CLOUD ENVIRONMENT

Cloud technology usage in nowadays companies constantly grows every year. Moreover, the COVID-19 situation caused even a higher acceleration of cloud adoption. A higher portion of deployed cloud services, however, means also a higher number of exploitable attack vectors. For that reason, risk assessment of the cloud environment plays a significant role for the companies. The target of this paper is to present a risk assessment method specialized in the cloud environment that supports companies with the identification and assessments of the cloud risks. The method itself is based on ISO/IEC 27005 standard and addresses a list of predefined cloud risks. Besides, the paper also presents the risk score calculation definition. The risk assessment method is then applied to an accounting company in a form of a case study. As a result, 24 risks are identified and assessed within the case study where each risk included also exemplary countermeasures. Further, this paper includes a description of the selected cloud risks. © 2022 Slovak Academy of Sciences. All rights reserved.

Floating Structure Integrity Management Program (FSIMP) Towards Digitalization

The objective of the paper is to demonstrate digitalization of Floating Structures Integrity Management Program (FSIMP) and its application for the structural integrity of floating structure assets. The framework of FSIMP is being developed by adopting Risk Based Inspection (RBI) methodology and complemented with technical know-how and industry best-practices. Implementing the methodology provides strategic planning for maintenance by reducing the anticipated risk. Hence, ensuring uninterrupted service of the floating structure assets throughout the service life. This paper presents a systematic approach for digitalization of the integrity management program for a nominated floating structure asset. The methodology offers a procedure to acquire necessary data management gathering, risk assessment, and RBI survey plan to maintain the structural integrity in the centralized web-based platform of FSIMP. RBI process is adopted into the FSIMP to investigate all deterioration and failure mechanisms. These structures will be identified by qualitative and quantitative risk assessment methods. The implementation of FSIMP offers a wide range of capabilities in structural integrity management such as integrating all floating structure fleet assets in a single dashboard of web-based platform, clear line of sight for reliable structural integrity, and an holistic overview across all levels of management. FSIMP with RBI methodology evaluates all data gathering to optimize inspection resources based on the risk assessment through an optimum combination of inspection methods and frequencies. The whole process is aligned to the requirements from Classification to ensure reliability for continuous operations. It also observes the essential need of digitalization for FSIMP during the time of post-COVID19 pandemic and the ever-expanding offshore oil, gas and energy frontiers that demand the adoption of new and advanced technologies, especially in the field of digitalization. It is shown that FSIMP has great potential as a digitalization tool and system to integrate with the RBI risk assessment that aligns to the requirements from Classification. It is strategically to maximize the effectiveness and improved efficiency for inspection and monitoring plan. The paper provides information on the solution of digitalization to the Floating Structures Integrity Management Program (FSIMP) in ensuring that the integrity of floating structure asset during the service life is intact for continuous operation and a holistic overview for all the assigned fleet assets in a centralized dashboard web-based platform. In addition to that, RBI is as added benefit to the FSIMP with its structure methodology of data evaluation and risk assessment in order to objectively optimizing inspection and maintenance resources. Copyright © 2023, International Petroleum Technology Conference.

MCMARS: Hybrid Multi-criteria Decision-Making Algorithm for Recommender Systems of Mobile Applications

A mobile application (app) recommender system needs to support both developers and users. Existing recommender systems in the literature are based on single-criterion analysis, which is insufficient for producing better recommendations. Moreover, recommendations do not reflect the user's perspectives. To address these issues, in this paper, we present a Multi-Criteria Mobile App Recommender System (MCMARS) that assists developers in improving their apps and recommends the top-performing apps to users. We define the performance score of an app based on four criteria attributes: risk assessment score, functionality score, user rating, and the app's memory size. We define the risk assessment score for each app using multi-perspective analysis and the functionality score by assigning preference weights to the services of apps in the same category. We evaluate optimal weights of the criteria by integrating the entropy method and the extended Best-Worst method (BWM) using Hesitant-Triangular-Fuzzy information with group-decisions. Finally, the TOPSIS uses these weights to assess the app's performance. To validate our MCMARS, we prepared a dataset of 124 government-approved COVID-19 Android apps from 80 countries and made it available on GitHub for the research community. Finally, we perform a fine-grained analysis of the app's performance based on the criteria attributes that help the developers to improve their apps. The experimental results show that two independent attributes, "risk assessment score” and "functionality score”, significantly measure the app's performance. According to our findings, only 12.5% of the apps in the experimental dataset provide high-performance, high-functionality, and low-risk. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Mathematical Model of the Risk Assessment for Airborne Transmission in a Classroom with a Ventilation System and Nine Distinct Face Mask Efficiency

TB, COVID-19, MERS, and SARS are all serious infectious diseases that are transmitted by the air or aerosol via coughing, spitting, sneezing, speaking, or wounds. When restaurants and bars reopen and continue operations in some parts of the United States, the Centers for Disease Control and Prevention (CDC) gives the following suggestions for how operators can reduce risk for employees, customers, and communities while also restricting the spread of COVID-19. The more and longer a person interacts with others, the greater the risk of COVID-19 spreading. Therefore, we need to be informed of its management and treatment. As a result, for the control and reduction of potentially polluted air, such as CO2 levels, good air quality management is required. They investigated the protective effectiveness of face masks against airborne transmission of infectious SARS-CoV-2 droplets and aerosols in response to the World Health Organization's recommendation to wear face masks to prevent the spread of COVID-19. Using nine different forms of mask efficiency, this research provides a mathematical model for calculating the chance of airborne transmission in a classroom. The fourth-order Runge-Kutta approach is used to approximate the model solution. The proposed strategy strikes a balance between the number of students allowed to stay in the classroom and the effectiveness of nine different masks. We can see how utilizing nine different masks and a well-ventilated system in the classroom can help to reduce the risk of airborne infection. © 2023, IAENG International Journal of Applied Mathematics. All Rights Reserved.

An Edge Based Framework for Risk Assessment of Communicable Disease

Along with the development of edge computing and Artificial Intelligence (AI), there has been an explosion of health-care system. As COVID-19 spread globally, the pandemic created significant challenges for the global health system. Therefore, we proposed an edge-based framework for risk assessment of communicable disease called CDM-FL. The CDM-FL consists of two modules, the common data model (CDM) and federated learning (FL). The CDM can process and store multi-source heterogeneous data with standardized semantics and schema. This provides more data for model training using medical data globally. The model is deployed on edge nodes that can measure patients' status locally and with low latency. It also keeps patient privacy from being disclosed that patient are more likely to share their medical data. The results based on real-world data show that CDM-FL can help physicians to evaluate the risk of communicable disease as well as save lives during severe epidemic situations. © 2022 IEEE.

Seasonal variation and ecological risk assessment of Pharmaceuticals and Personal Care Products (PPCPs) in a typical semi-enclosed bay — The Bohai Bay in northern China

The Bohai Bay as a typical semi-enclosed bay in northern China with poor water exchange capacity and significant coastal urbanization, is greatly influenced by land-based inputs and human activities. As a class of pseudo-persistent organic pollutants, the spatial and temporal distribution of Pharmaceuticals and Personal Care Products (PPCPs) is particularly important to the ecological environment, and it will be imperfect to assess the ecological risk of PPCPs for the lack of systematic investigation of their distribution in different season. 14 typical PPCPs were selected to analyze the spatial and temporal distribution in the Bohai Bay by combining online solid-phase extraction (SPE) and HPLC-MS/MS techniques in this study, and their ecological risks to aquatic organisms were assessed by risk quotients (RQs) and concentration addition (CA) model. It was found that PPCPs widely presented in the Bohai Bay with significant differences of spatial and seasonal distribution. The concentrations of ∑PPCPs were higher in autumn than in summer. The distribution of individual pollutants also showed significant seasonal differences. The high values were mainly distributed in estuaries and near-shore outfalls. Mariculture activities in the northern part of the Bohai Bay made a greater contribution to the input of PPCPs. Caffeine, florfenicol, enrofloxacin and norfloxacin were the main pollutants in the Bohai Bay, with detection frequencies exceeding 80 %. The ecological risk of PPCPs to algae was significantly higher than that to invertebrates and fish. CA model indicated that the potential mixture risk of total PPCPs was not negligible, with 34 % and 88 % of stations having mixture risk in summer and autumn, respectively. The temporary stagnation of productive life caused by Covid-19 weakened the input of PPCPs to the Bohai Bay, reducing the cumulative effects of the pollutants. This study was the first full-coverage investigation of PPCPs in the Bohai Bay for different seasons, providing an important basis for the ecological risk assessment and pollution prevention of PPCPs in the bay. © 2022 Elsevier B.V.

Detecting Open Banking API Security Threats Using Bayesian Attack Graphs

Particularly amid Covid-19, enterprises' digital transformation has rapidly accelerated, making cybersecurity an even bigger challenge. Financial institutions adopt FinTech technologies to advance their service and achieve an enhanced customer experience that creates a competitive edge in the market. FinTech products utilise open banking API services to allow communication between a financial institution and a FinTech provider. However, such an integration introduces significant security concerns. Therefore, financial firms must ensure that a robust API service to protect the bank's infrastructure and its customers' information. To address this concern, we propose a Framework for Open Banking API security that utilises STRIDE model to identify security threats in FinTech integration via Open Banking API and Bayesian Attack Graphs to automate predictions of the most exploitable attack paths. © 2022 IEEE.

Commodity supply risk assessment of China's copper industrial chain: The perspective of trade network

With the rapid development of China's new energy industry, the consumption demand for copper resources is increasing. As a key raw material, copper resources are becoming increasingly important. Taking the demand for copper commodities in China's new energy development as the research background and the international trade environment and pattern of copper supply as the research perspective, this paper makes an overall assessment of the commodity supply risk of China's copper industrial chain from 2010 to 2021 using the complex network and the newly established three-dimensional risk assessment model and finally reaches the following conclusions. The supply risk of commodities in China's copper industrial chain has been rising continuously since 2019 after experiencing fluctuating development in the early stage and a continuous decline in recent years, and there may be a trend of continuing to rise. The supply risk of China's copper industrial chain was gradually reduced from upstream to midstream and downstream, and the supply risk of copper smelting was more severe. The disruption potential risk of China's copper industrial chain was relatively low, and the international import market structure of copper commodities was relatively reasonable. The supply risk characteristics of each link in China's copper industrial chain were different. Due to the influence of import dependence, the copper mining industry had a high risk of trade exposure. However, the smelting and copper processing industries had certain limitations in production management, operation management and technology research and development, and their ability to withstand risks was weak. In addition, the impact of the domestic COVID-19 epidemic ha caused a high industrial chain vulnerability risk. © 2023 Elsevier Ltd