Effective Risk Mitigation Strategies Research Articles

The role of uncertainties in the seismic assessment of masonry churches affected by compound rocking failure mechanism: Macro-block limit analysis investigations

Façade overturning is one of the most common failure mechanisms observed in single-nave masonry churches when subjected to seismic action. Several factors, including geometry, masonry patterns, mechanical properties, and loading conditions, influence the force and displacement capacities of these masonry churches. This study aims to assess the impact of various model parameters on the seismic assessment of single-nave masonry churches subjected to the compound rocking failure mechanism by adopting macro-block limit analysis. The analysis of variance (ANOVA) is employed to investigate the influence of geometrical and mechanical parameters under the assumption of complete knowledge of the structure. Furthermore, probabilistic analysis explores how incomplete knowledge may affect the structural assessment of the compound rocking failure mechanism in single-nave masonry churches. The findings of this study highlight the importance of accurate modelling and surveying in evaluating the seismic vulnerability of single-nave masonry churches and can be useful for developing effective seismic risk mitigation strategies.

Boosting the Organizational Performance: Ethical Climate as a Moderator for Optimizing Risk Management Practices in the Saudi Public Sector

Purpose: Drawing upon contingency theory, this study aims to investigate the relationship between risk management practices (RMPs), ethical climate (EC), and organizational performance (OP) in public organizations within the Kingdom of Saudi Arabia (KSA). Specifically, the study explores the moderating effect of EC in shaping the relationship between RMPs and OP. Design/methodology/approach: Survey data were collected from a convenience sampling of 392 senior government directors within public organizations in the KSA through an online questionnaire. The relationships between study constructs were analyzed using PLS-SEM with the bootstrapping technique. Findings: The study's findings reveal a significant positive impact of both RMPs and EC on organizational performance. Notably, the research demonstrates that ethical climate significantly moderates the positive relationship between RMPs and OP, highlighting the critical importance of ethical conduct in maximizing the effectiveness of risk mitigation strategies. Research limitations/implications: The study's focus on the Saudi public sector and its use of a convenience sampling technique, primarily comprising senior government directors, are significant limitations when interpreting this study's findings. Expanding research to incorporate diverse organizational settings could significantly enhance this work's generalizability and practical implications. Additionally, focusing solely on EC as a moderator in the RMP-OP relationship overlooks other potentially relevant factors. Future research could delve deeper into the mechanisms through which ethical climate strengthens the impact of RMPs on performance, providing valuable guidance for organizational interventions. Originality/value: This study's originality lies in its first-time examination of ethical climate as a moderator in the relationship between RMPs and organizational performance within Saudi public organizations. Further, study findings expanded the scope of contingency factors demonstrating that a strong ethical climate significantly amplifies the positive impact of RMPs on performance, providing compelling evidence for considering ethical climate as a crucial contingency factor for public sector success.

Species-specific eDNA assay development for enhanced box jellyfish risk management in coastal environments

Human interaction with marine creatures holds both positive and negative dimensions. Coastal communities benefit from marine environments, relying on them for sustenance and livelihoods. Fishing activities support economies, and marine biodiversity contributes to overall ecosystem health. However, challenges like overfishing, habitat destruction, and pollution pose threats to both marine life and human communities. Recently, there has been widespread concern regarding the potential increase in jellyfish populations across global marine ecosystems, attributed mainly to environmental factors such as climate drivers and anthropogenic forces, or their complex interactions. Encounters with hazardous marine species, such as box jellyfish, exemplify the dangers associated with coastal activities. Unintended interactions may lead to stings, injuries, and even fatalities, necessitating proactive measures and advanced technologies. This study addresses the inadequacies of existing measures in preventing box jellyfish incidents by introducing environmental DNA (eDNA) assays for detecting the deadly Chiropsoides buitendijki and focuses on developing qPCR and dPCR-based eDNA assays. Emphasising prevention over treatment, the study establishes a proactive system to assess C. buitendijki distribution across 63 tourist beaches in the Gulf of Thailand. Comparative analysis highlights the superior performance of dPCR over qPCR and traditional surveys. The dPCR experiment yielded positive results for all eDNA samples collected at sites where C. buitendijki had previously been identified. Remarkably, the eDNA testing also detected positive results in 16 additional sample locations where no physical specimens were collected, despite reported jellyfish stings at some of these sites. These findings underscore the precision and efficacy of the proposed eDNA detection technology in the early detection and assessment of box jellyfish distribution. This advancement therefore not only aids ecological research but also serves as a valuable tool for safeguarding public health, providing an early warning system for potential jellyfish encounters. Balancing positive human-marine interactions with effective risk mitigation strategies is crucial for sustainable coexistence, the preservation of marine ecosystems, and human well-being.

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

Evaluating the crash risk of powered two-wheelers in urban mixed traffic environments: a conflict threshold perspective

The study investigates the crash risk of powered two-wheelers (PTWs) involved in multiple conflict types, with different vehicle classes constituting a mixed traffic stream. This study uses the extreme value theory to estimate the crash risk after establishing the conflict thresholds for potential rear-end and side-swipe conflicts by accounting for interacting vehicle types. The study considers four vehicle pairs involving PTWs: PTW-PTW, PTW-MThW (motorized three-wheeler), PTW-Car, and PTW-Bus. The study found that the conflict thresholds corresponding to rear-end and side-swipe types increase with the interacting vehicle size. The crash risk is lowest for the PTW-PTW pair (0.315%) in rear-end conflicts, whereas the risk is the highest for the PTW-MThW pair (3.7%) in side-swipe conflicts. The crash risk corresponding to the PTW interacting with other vehicle types is higher than that of the PTW-PTW pair. Hence, the implementation of exclusive PTW lanes could be an effective risk mitigation strategy for PTW-dominant mixed traffic environments.

Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests

Earthquakes pose significant risks globally, necessitating effective seismic risk mitigation strategies like earthquake early warning (EEW) systems. However, developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations. This study examines a deep-learning-based on-site EEW framework known as ROSERS (Real-time On-Site Estimation of Response Spectra) proposed by the authors, which constructs response spectra from early recorded ground motion waveforms at a target site. This study has three primary goals: (1) evaluating the effectiveness and applicability of ROSERS to subduction seismic sources; (2) providing a detailed interpretation of the trained deep neural network (DNN) and surrogate latent variables (LVs) implemented in ROSERS; and (3) analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations. ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions. Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database, especially given the peculiarities of the subduction seismic environment. The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships. Finally, the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity. The results indicate that on-site predictions can be considered reliable within a 2–9 km radius, varying based on the magnitude and distance from the earthquake source. This information can assist end-users in strategically placing sensors, minimizing blind spots, and reducing errors from regional extrapolation.

A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.

Anticipating a risky future: long short-term memory (LSTM) models for spatiotemporal extrapolation of population data in areas prone to earthquakes and tsunamis in Lima, Peru

Abstract. In this paper, we anticipate geospatial population distributions to quantify the future number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru. We capitalize upon existing gridded population time series data sets, which are provided on an open-source basis globally, and implement machine learning models tailored for time series analysis, i.e., based on long short-term memory (LSTM) networks, for prediction of future time steps. Specifically, we harvest WorldPop population data and teach LSTM and convolutional LSTM models equipped with both unidirectional and bidirectional learning mechanisms, which are derived from different feature sets, i.e., driving factors. To gain insights regarding the competitive performance of LSTM-based models in this application context, we also implement multilinear regression and random forest models for comparison. The results clearly underline the value of the LSTM-based models for forecasting gridded population data; the most accurate prediction obtained with an LSTM equipped with a bidirectional learning scheme features a root-mean-squared error of 3.63 people per 100 × 100 m grid cell while maintaining an excellent model fit (R2= 0.995). We deploy this model for anticipation of population along a 3-year interval until the year 2035. Especially in areas of high peak ground acceleration of 207–210 cm s−2, the population is anticipated to experience growth of almost 30 % over the forecasted time span, which simultaneously corresponds to 70 % of the predicted additional inhabitants of Lima. The population in the tsunami inundation area is anticipated to grow by 61 % until 2035, which is substantially more than the average growth of 35 % for the city. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.

Antarctic Snow Failure Mechanics: Analysis, Simulations, and Applications.

Snow failure is the process by which the stability of snow or snow-covered slopes is destroyed, resulting in the collapse or release of snow. Heavy snowfall, low temperatures, and volatile weather typically cause consequences in Antarctica, which can occur at different scales, from small, localized collapses to massive avalanches, and result in significant risk to human activities and infrastructures. Understanding snow damage is critical to assessing potential hazards associated with snow-covered terrain and implementing effective risk mitigation strategies. This review discusses the theoretical models and numerical simulation methods commonly used in Antarctic snow failure research. We focus on the various theoretical models proposed in the literature, including the fiber bundle model (FBM), discrete element model (DEM), cellular automata (CA) model, and continuous cavity-expansion penetration (CCEP) model. In addition, we overview some methods to acquire the three-dimensional solid models and the related advantages and disadvantages. Then, we discuss some critical numerical techniques used to simulate the snow failure process, such as the finite element method (FEM) and three-dimensional (3D) material point method (MPM), highlighting their features in capturing the complex behavior of snow failure. Eventually, different case studies and the experimental validation of these models and simulation methods in the context of Antarctic snow failure are presented, as well as the application of snow failure research to facility construction. This review provides a comprehensive analysis of snow properties, essential numerical simulation methods, and related applications to enhance our understanding of Antarctic snow failure, which offer valuable resources for designing and managing potential infrastructure in Antarctica.

A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition.

The link between metabolic syndrome (MetS) and neurodegenerative as well as cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, brain morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis, we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.

A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition

The link between metabolic syndrome (MetS) and neurodegenerative as well as cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, brain morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis, we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.

The Influence of Risk Management Practices on Financial Market Stability: Insights from Lebanon

This research investigates the relationship between various risk management practices, including diversification, hedging, and contingency planning, and their impact on the dependent variable, financial market volatility. The study is based on a sample size of 127 observations in the context of Lebanon's financial markets. The primary objective of this research is to analyze how risk management practices employed by financial institutions and market participants affect the stability of Lebanon's financial markets. To achieve this, the study employs a quantitative approach, utilizing statistical analysis and regression modeling to assess the relationships between the independent variables (risk management practices) and the dependent variable (financial market volatility). The independent variables in this study encompass risk management practices, namely diversification, hedging, and contingency planning. These practices are crucial components of financial risk management strategies employed by institutions and investors. By examining their influence on financial market stability, this research aims to provide insights into effective risk mitigation strategies within the Lebanese financial context. The decision of Lebanon as the study's context is significant due to the unique economic and geopolitical challenges faced by the country. The financial markets in Lebanon have been subject to various external and internal pressures, making the analysis of risk management practices particularly relevant. The sample size of 127 observations allows for robust statistical analysis, ensuring the validity of the findings. The research findings are expected to contribute to the existing body of knowledge on risk management and financial market stability, with potential implications for policymakers, financial institutions, and investors operating in Lebanon and similar emerging market economies.

IPOs as Catalysts for Retail Investor Wealth Creation: Evidence from the Indian Stock Market

The study delves into the dynamics of wealth creation for retail investors in the context of Initial Public Offerings (IPOs) within the Indian equity markets. Against the backdrop of a burgeoning retail investor base, the research offers a comprehensive analysis spanning April 2012 to December 2022, evaluating short-term and long-term IPO performance metrics. The study reveals nuanced insights into wealth creation dynamics by scrutinizing listing day returns, first-week to third-year returns, and market-adjusted returns vis-à-vis the Nifty50 Index. Notably, the research suggests that while IPOs exhibit promise with positive listing day returns, sustaining growth over extended horizons poses challenges. The hypothesis of inflation serving as a pragmatic hurdle rate for retail investors is explored, offering a novel perspective on wealth preservation. It also sheds light on the significant variability in the Real Rate of Returns (RRRs) among IPOs, with some contributing to wealth generation while others result in wealth erosion. This underscores the importance of retail investors carefully assessing and managing risks, emphasizing the need for prudent portfolio diversification and effective risk mitigation strategies, particularly over longer investment horizons.

Quantifying modeling uncertainties in seismic analysis of dams: Insights from an international benchmark study

AbstractAdvances in nonlinear dynamic analysis of dams have not completely resolved concerns over modeling confidence and analysis accuracy. Verification and validation offer accuracy assessment, but uncertainties persist during performance evaluation due to both epistemic (modeling) and aleatory (parametric) sources. Epistemic uncertainties arise from simplifications and modeling techniques. This paper addresses epistemic uncertainties in a gravity dam seismic analysis using data from the International Comnission on Large Dams (ICOLD) benchmark study. While the benchmark formulation included the finite element model of the dam, mechanical material properties, and dynamic loads, participants retained the flexibility to opt for best‐practice modeling assumptions, simplifications, and other specifics. Notable response variability emerged, particularly in crack profiles and damage predictions. This study examines sources of variability, quantifying modeling uncertainty for the benchmark problem. More specifically, the modeling variability is quantified using the logarithmic standard deviation, also known as dispersion. This metric enables its incorporation into other seismic risk assessment and fragility studies. Under relatively low‐intensity motion (peak ground acceleration [PGA] of 0.18 g in this case), modeling dispersion of 0.45, 0.30, 0.32, and 0.30 were calculated for the maximum dynamic crest displacement, maximum hydrodynamic pressure at the heel, heel and crest maximum acceleration, respectively. Additionally, the dispersion of the failure PGA was determined to be 0.7. Findings underscore the need for systematic seismic response modeling in dam engineering to enhance prediction accuracy. A better understanding of the sources and magnitudes of modeling uncertainties can help improve the reliability of dam seismic analysis and contribute to the development of more effective risk assessment and mitigation strategies.

HPB P18 Continuous drain irrigation as a risk mitigation strategy for postoperative pancreatic fistula; a meta-analysis

Abstract Background Postoperative Pancreatic Fistula (POPF) formation serves as the principle cause for the significant morbidity and mortality rates observed following pancreatectomy. Several different POPF mitigation strategies have been previously trialled, with variable success. Continuous drain irrigation as a treatment strategy for infected pancreatic necrosis has previously been described as an effective treatment strategy, however its role following pancreatectomy has yet to be determined. Methods A meta-analysis of the pre-existing literature was performed. The primary endpoint was determining whether continuous drain irrigation reduced POPF rates following pancreatectomy. Secondary endpoint evaluated its impact on post operative morbidity, mortality and length of stay. Analysis of dichotomous variables (complication rates) was calculated utilising the odds ratio (OR) with 95% confidence interval (CI) with the Mantel–Haenszel method with a random effects model. Continuous data (length of stay) was assessed using mean difference (MD) and 95% CI with the inverse variance random effects model. Data was tabulated and visualised through the creation of forest plots. Results Nine articles involving 782 patients was included in this meta-analysis. Continuous drain irrigation use was associated with a statistically significant reduction in POPF rates (OR (95% CI) 0.40 (0.19-0.82), p=0.01). Upon subgroup analysis, a significant reduction in clinically relevant POPF was also noted (OR 0.37 (0.20-0.66), p = 0.0008). A reduction in post operative complications was also observed: delayed gastric emptying (0.45 (0.24-0.84), p = 0.01) and the need for re-operation (0.33 (0.11-0.96), p = 0.04). This reduction in post operative complications rates also translated into a reduced post operative length of stay (MD -2.62 (-4.97 - -0.26), p =0.03). Conclusions Continuous drain irrigation following pancreatectomy serves as an effective risk mitigation strategy against POPF.

Source-specific probabilistic contamination risk and health risk assessment of soil heavy metals in a typical ancient mining area

Heavy metal pollution (HMP) from mining operations severely threatens soil ecosystems and human health. Identifying the sources of soil heavy metals (HMs) and assessing source-specific risks are critical for developing effective risk mitigation strategies. In this study, a combination of methodologies including PMF, Monte Carlo analysis, soil pollution risk index, and a human health risk assessment model were utilized to investigate soil HM risks in a typical ancient mining area in Daye City, China, considering both environmental pollution and human health impacts. Cu emerged as the most significant soil pollution risk, whereas As posing the highest health risk. About 48.44 % of the multi-element integrated soil pollution risk has escalated to the heavy level. Furthermore, around 22.42 % of the non-carcinogenic risk (NCR) and 9.53 % of the carcinogenic risk (CR) exceeded unacceptable thresholds (THI > 1 for NCR and TCR > 1E-4 for CR). The PMF model identified four distinct sources: the smelting industry, traffic emissions, a combination of agricultural and natural factors, and mining activities. The mixed agricultural and natural source significantly impacted health risks, contributing 42.17 % to NCR and 53.88 % to CR, followed by the mining source, contributing 31.67 % to NCR and 24.07 % to CR. Interestingly, the mining source contributed the highest soil pollution risk at 42.45 %, while the mixed agricultural and natural source exhibited the lowest at 16.33 %. Furthermore, the study explored source-specific risk components by evaluating the contributions of different sources to specific elements. The mining source was identified as the focus for soil HMP control, followed by the mixed agricultural and natural source. Overall, this study provided an in-depth analysis of soil heavy metal risks in mining areas from the source apportionment perspective, which broadened the research framework of soil heavy metal source analysis and risk assessment, potentially providing scientific guidance for managing regional soil HMP.

Predicting Rainfall-Induced Landslides in Muriwai, Auckland: A Comprehensive Multi-Model Approach for Enhanced Risk Management

Landslide studies hold significant importance in understanding and mitigating the risks associated with these geohazards. Particularly in regions like New Zealand, where diverse topography and climatic conditions contribute to frequent landslide occurrences, such studies are crucial. This research focuses on Muriwai, Auckland, a region that has experienced substantial landslide activity triggered by rainfall. The study adopts a multi-model approach, integrating both empirical and process-based models to predict rainfall-induced landslides. This approach leverages the strengths of individual models, including logistic regression, random forest, support vector machines, artificial neural networks, and decision trees, thereby enhancing the robustness and accuracy of predictions. A comprehensive dataset, comprising historical landslide records, climatic data, and terrain and geological data, is used to train and validate these models. The data is spatially aligned within a unified Geographic Information System (GIS) database, ensuring consistency and accuracy in the analysis. The multi-model ensemble provides a probabilistic prediction of landslide occurrence, which is visualized as a landslide susceptibility map. This map serves as a valuable tool for understanding the spatial distribution of landslide risks in Muriwai. The performance of each model and the ensemble is evaluated using several metrics, ensuring the reliability of the predictions. The results are interpreted to understand the influence of different factors, particularly climatic parameters, on landslide occurrences. This study's findings contribute to effective landslide risk management and mitigation strategies in Muriwai, Auckland, and provide valuable insights for similar studies in other regions.

The BioCascade-VIVAS system for collection and delivery of virus-laden size-fractionated airborne particles

The size of virus-laden particles determines whether aerosol or droplet transmission is dominant in the airborne transmission of pathogens. Determining dominant transmission pathways is critical to implementing effective exposure risk mitigation strategies. The aerobiology discipline greatly needs an air sampling system that can collect virus-laden airborne particles, separate them by particle diameter, and deliver them directly onto host cells without inactivating virus or killing cells. We report the use of a testing system that combines a BioAerosol Nebulizing Generator (BANG) to aerosolize Human coronavirus (HCoV)-OC43 (OC43) and an integrated air sampling system comprised of a BioCascade impactor (BC) and Viable Virus Aerosol Sampler (VIVAS), together referred to as BC-VIVAS, to deliver the aerosolized virus directly onto Vero E6 cells. Particles were collected into four stages according to their aerodynamic diameter (Stage 1: >9.43 μm, Stage 2: 3.81–9.43 μm, Stage 3: 1.41–3.81 μm and Stage 4: <1.41 μm). OC43 was detected by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) analyses of samples from all BC-VIVAS stages. The calculated OC43 genome equivalent counts per cm3 of air ranged from 0.34 ± 0.09 to 70.28 ± 12.56, with the highest concentrations in stage 3 (1.41–3.81 μm) and stage 4 (<1.41 μm). Virus-induced cytopathic effects appeared only in cells exposed to particles collected in stages 3 and 4, demonstrating the presence of viable OC43 in particles <3.81 μm. This study demonstrates the dual utility of the BC-VIVAS as particle size-fractionating air sampler and a direct exposure system for aerosolized viruses. Such utility may help minimize conventional post-collection sample processing time required to assess the viability of airborne viruses and increase the understanding about transmission pathways for airborne pathogens.

Flood vulnerability assessment: an effective tool to evaluate the lifecycle risk analysis of bridges.

AbstractFrom a lifecycle perspective, assessing bridge vulnerability is a key element of implementing effective risk mitigation and management strategies. This study offers an integrated and computationally efficient approach to evaluate bridges' vulnerability to failure due to hydrodynamic and wood debris forces and the concurrent action of local scour. The vulnerability analysis follows a stochastic approach and is developed through a case‐study analysis of a bridge that has a high proneness to flood‐related hazards. The modelling of the wood debris accumulation is accomplished based both on a Standard approach and on findings drawn from up‐to‐date research data. It is found that the shape of wood debris accumulation envisioned in the first approach has a particularly adverse effect on the bridge's vulnerability, exposing its limitations. A detailed and comprehensive methodology for assessing the risk related to flood hazards on bridges is also presented. It is perceived that not only will this new tool be valuable in estimating the flood loss assessment on bridges, but it will also be beneficial for facilitating decision‐making processes and in finding effective countermeasures for risk mitigation of flood‐related hazards.

Leverage Bayesian Network and Fault Tree Method on Risk Assessment of LNG Maritime Transport Shipping Routes: Application to the China–Australia Route

The China–Australia Route, which serves as the southern economic corridor of the ‘21st Century Maritime Silk Road’, bears great importance in safeguarding maritime transportation operations. This route plays a crucial role in ensuring the security and efficiency of such activities. To pre-assess the risks of this route, this paper presents a two-stage analytical framework that combines fault tree analysis and Bayesian network for evaluating the occurrence likelihood of risk of transporting liquefied natural gas (LNG) on the China–Australia Route. In the first stage, our study involved the identification of 22 risk influencing factors drawn from a comprehensive review of pertinent literature and an in-depth analysis of accident reports. These identified factors were then utilized as basic events to construct a fault tree. Later, we applied an expert comprehensive evaluation method and fuzzy set theory, and by introducing voting mechanism into expert opinions, the prior probability of basic events was calculated. In the second stage, a fault tree was transformed into a Bayesian network, which overcame the deficiency that the structure and conditional probability table of the Bayesian network find difficult to determine. Consequently, the employment of the Bayesian network architecture was applied to forecast the likelihood of LNG maritime transport along the China–Australia shipping pathway. The probability importance and critical importance of each basic event was calculated through an importance analysis. The development of a risk matrix was achieved by considering the two primary dimensions of frequency and impact, which were subsequently utilized to categorize all relevant risk factors into high, moderate, or low risk categories. This allowed for effective risk mitigation and prevention strategies to be implemented. Finally, assuming that the final risk occurs, we calculated the posterior probability of the basic event to diagnose the risk. The research findings indicate that the primary reasons for the risk of transporting LNG on the China–Australia Route are the impact of natural forces and epidemics, piracy and terrorist attacks, and the risk of LNG explosions. In the final section, we provide suggestions and risk control measures based on the research results to reduce the occurrence of risks.