Risk Mitigation Research Articles

Critical risk management strategies for the operation of public–private partnerships: a vulnerability perspective of infrastructure projects

PurposeThe success of public–private partnership (PPP) projects is highly correlated to the successful management of risks encountered during the operation phase. PPP projects are especially exposed to risk due to the long operation period over which revenues need to be generated to recoup substantial initial investment and operational running costs. Despite the critical impact of risk exposure, limited research has been specifically undertaken on the matter of operational risk management. This study seeks to address this oversight by identifying and evaluating operational risk management strategies for PPPs.Design/methodology/approachVulnerability theory is the theoretical lens used, with context drawn from Chinese PPP projects. Based on the data collected from expert interviews and questionnaires, 28 operational risk management strategies are identified. A fuzzy synthetic method is employed to analyze the effectiveness of the 28 strategies.FindingsThe findings reveal that providing an exit mechanism clause into the contract, establishing a comprehensive performance evaluation mechanism and developing a clear compensation mechanism are the top three effective strategies. This study also reveals that risk mitigation approaches that reduce vulnerability prove more effective than attempts to reduce external threats. Specifically, strategies aimed at managing contract, political, technical and financial risk are the most effective.Originality/valueThe findings of this study extend current knowledge regarding the risk management of PPP projects. They also offer a reference by which practitioners may select effective operational risk management pathways and thereby, galvanize the sustainable development of PPPs.

Impact of previous treatment history and B-cell depletion treatment duration on infection risk in relapsing-remitting multiple sclerosis: a nationwide cohort study

BackgroundB-cell depletion displays striking effectiveness in relapsing-remitting multiple sclerosis (RRMS), but is also associated with increased infection risk. To what degree previous treatment history, disease-modifying therapy (DMT) switching pattern and...

Inspiration4 data access through the NASA Open Science Data Repository

The increasing accessibility of commercial and private space travel necessitates a profound understanding of its impact on human health. The NASA Open Science Data Repository (OSDR) provides transparent and FAIR access to biological studies, notably the SpaceX Inspiration4 (I4) mission, which amassed extensive data from civilian astronauts. This dataset encompasses omics and clinical assays, facilitating comprehensive research on space-induced biological responses. These data allow for multi-modal, longitudinal assessments, bridging the gap between human and model organism studies. Crucially, community-driven data standards established by NASA’s OSDR Analysis Working Groups empower artificial intelligence and machine learning to glean invaluable insights, guiding future mission planning and health risk mitigation. This article presents a concise guide to access and analyze I4 data in OSDR, including programmatic access through GLOpenAPI. This pioneering effort establishes a precedent for post-mission health monitoring programs within space agencies, propelling research in the burgeoning field of commercial space travel’s impact on human physiology.

Liver paired exchange U.S. single center experience - pairs, chains, and use of compatible pairs'.

In the United States, the discrepancy between organ availability and need has persisted despite changes in allocation, innovations in preservation, and policy initiatives. Living donor liver transplant (LDLT) remains an underutilized means of improving access to timely liver transplantation and decreasing waitlist mortality. Liver paired exchange (LPE) represents an opportunity to overcome LDLT pair incompatibility due to size, anatomy, or blood type. LPE was adopted as a strategy to augment access to liver transplantation at our institution. Specific educational materials, consent forms, and selection processes were developed to facilitate LPE. From 2019 through October 2023, our center performed 11 LPEs, resulting in 23 LDLT pairs. The series included several types of LPE: those combining complementary incompatible pairs, the inclusion of compatible pairs to overcome incompatibility, and the use of altruistic non-directed donors to initiate chains. These exchanges facilitated transplantation for 23 recipients, including 1 pediatric patient. LPE improved access to liver transplantation at our institution. The ethical application of LPE includes tailored patient education, assessment and disclosure of exchange balance, mitigation of risk and maximization of benefit for donors and recipients.

Numerical simulations of dam-break flows of viscoplastic fluids via shallow water equations

Abstract This paper presents simulations of dam-break flows of Herschel–Bulkley viscoplastic fluids over complex topographies using the shallow water equations (SWE). In particular, this study aims to assess the effects of rheological parameters: power-law index (n), consistency index (K), and yield stress ($$\tau _{c}$$ τ c ), on flow height and velocity over different topographies. Three practical examples of dam-break flow cases are considered: a dam-break on an inclined flat surface, a dam-break over a non-flat topography, and a dam-break over a wet bed (downstream containing an initial fluid level). The effects of bed slope and depth ratios (the ratio between upstream and downstream fluid levels) on flow behaviour are also analyzed. The numerical results are compared with experimental data from the literature and are found to be in good agreement. Results show that for both dry and wet bed conditions, the fluid front position, peak height, and mean velocity decrease when any of the three rheological parameters are increased. However, based on a parametric sensitivity analysis, the power-law index appears to be the dominant factor in dictating fluid behaviour. Moreover, by increasing the bed slope and/or depth ratio, the wave-frontal position moves further downstream. Furthermore, the presence of an obstacle is observed to cause the formation of an upsurge that moves in the upstream direction, which increases by increasing any of the three rheological parameters. This study is useful for an in-depth understanding of the effects of rheology on catastrophic gravity-driven flows of non-Newtonian fluids (like lava or mud flows) for risk assessment and mitigation. Graphical abstract

DivFolio: a Shiny application for portfolio divestment in green finance wealth management

Abstract This paper introduces DivFolio, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of DivFolio. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.

Utilizing stem cell-secreted molecules as a versatile toolbox for skin regenerative medicine

Stem cells are recognized as an important target and tool in regenerative engineering. In this study, we explored the feasibility of engineering amniotic fluid-derived mesenchymal stem cell-secreted molecules (afMSC-SMs) as a versatile bioactive material for skin regenerative medicine applications in a time- and cost-efficient and straightforward manner. afMSC-SMs, obtained in powder form through ethanol precipitation, effectively contributed to preserving the self-renewal capacity and differentiation potential of primary human keratinocytes (pKCs) in a xeno-free environment, offering a potential alternative to traditional culture methods for their long-term in vitro expansion, and allowed them to reconstitute a fully stratified epithelium sheet on human dermal fibroblasts. Furthermore, we demonstrated the flexibility of afMSC-SMs in wound healing and hair regrowth through injectable hydrogel and nanogel-mediated transdermal delivery systems, respectively, expanding the pool of regenerative applications. This cell-free approach may offer several potential advantages, including streamlined manufacturing processes, scalability, controlled formulation, longer shelf lives, and mitigation of risks associated with living cell transplantation. Accordingly, afMSC-SMs could serve as a promising therapeutic toolbox for advancing cell-free regenerative medicine, simplifying their broad applicability in various clinical settings.

Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble

AbstractBase metals are key materials for various industrial sectors such as electronics, construction, manufacturing, etc. Their selling price is important both for the profitability of the mining and metallurgical companies that produce and trade them, as well as for the countries whose economies rely on their exports or tax revenues as a means for national income. Prices are also critical for companies that use base metals as inputs to fabricate end products. The prediction of prices’ future movements can serve as a tool for risk mitigation and better budget planning. In this study, the logarithmic returns of base metals are forecasted using an autoregressive Light Gradient Boosting Machine (LightGBM) as well as an ensemble comprising the aforementioned algorithm and a classical time series forecasting model (i.e., ARIMA). The two models are then compared to three simpler benchmark models, namely a global mean model, an exponential smoothing model and an ARIMA model. When comparing using RMSE, the autoregressive LightGBM model outperformed the three univariate benchmark models (and the ensemble) for forecasting 6 months ahead for aluminum and nickel returns, while copper and zinc returns were forecasted better by the ensemble. Neither of the proposed models performed better than an ARIMA model when it comes to forecasting lead and tin returns.

Capturing key interdependences among supply chain disruptions and mitigation strategies to enhance firm performance

PurposeThis paper aims to capture the complex interdependences between supply chain disruptions (SCDs), SC risk mitigation strategies and firm performance in the context of disruptive events to enhance resilience for medium-sized and large firms coping with complex supply chain networks. The roles of digitalization, insurance and government support have also been addressed as potential strategies to counteract the impacts of disruptions on supply chains.Design/methodology/approachThis study is based on an empirical investigation in an FMCG company – using a hybrid causal mapping technique based on the frameworks of interpretive structural modeling (ISM) and Bayesian networks (BN) – of 11 levels of relationships between SCDs (in supply, production, logistics, demand and finance), SC risk mitigation strategies (flexibility, efficiency, agility and responsiveness), insurance, government support, information and knowledge sharing, digitalization and finally the key firm performance measures (continuity, quality and financial performance).FindingsThe results of the empirical investigation reveal and describe: (1) the nature and probabilistic quantification of the lower-level relationships among the four SCDs, among the mitigation strategies and the three firm performance measures; (2) the nature and probabilistic quantification of the higher-level relationships among the impacts of SCDs, SC risk mitigation strategies and firm performance and (3) how to model and quantify the complex interdependences in single firms and their supply chains.Originality/valueOur results can support managers in developing more effective decision-making models to assess and manage unfavorable events and cascade effects among different functions and processes in the context of risks and disruptions.

DMLoc: Automatic Microseismic Locating Workflow Based on Deep Learning and Waveform Migration

Abstract During hydraulic fracturing, real-time acquisition of the spatiotemporal distribution of microseismic in the reservoir is essential in evaluating the risk of induced seismicity and optimizing injection parameters. By integrating deep learning with migration-based location methods, we develop an automatic microseismic locating workflow (named DMLoc). DMLoc applies deep learning to automate phase picking and leverage the phase arrival probability function generated by a convolutional network as the input for waveform migration. The proposed workflow is first applied to the continuous data of the Dawson-Septimus area. Compared with a reference catalog generated by the SeisComP3 software, our method automatically locates 57 additional seismic events (accounting for 43% of the events in the obtained catalog). We further evaluate the performance of DMLoc by applying it to a 35-day continuous microseismic dataset from the Tony Creek Dual Microseismic Experiment. The spatiotemporal distribution of our detected events is consistent with results reported in prior catalogs, demonstrating the effectiveness of our method. In contrast to using raw microseismic records for stacking, DMLoc addresses the issue of inaccurate locating caused by low signal-to-noise ratios and polarity changes. The use of GPUs has substantially accelerated the calculations and enabled DMLoc to output locating results in minutes. This fast and efficient metric could be easily extended to any microseismic monitoring scenario that requires (near) real-time locations and assists in site-based risk mitigation and industrial operation optimization.

Determinants of psychological distress during the COVID-19 pandemic among people who use drugs in Montreal, Canada.

Limited data exists on psychological impacts of the COVID-19 pandemic among people who use drugs (PWUD). This study aimed to determine the prevalence and correlates of severe psychological distress (PD) among PWUD in Montreal around the beginning of the pandemic. We conducted a rapid assessment study from May to December 2020 among PWUD recruited via a community-based cohort of people who inject drugs in Montreal (Hepatitis C cohort [HEPCO], N = 128) and community organisations (N = 98). We analysed self-reported data on changes in drug use behaviours and social determinants since the declaration of COVID-19 as a public health emergency, and assessed past-month PD using the Kessler K6 scale. Multivariable logistic regression was conducted to examine correlates of PD distress (score ≥13). Of 226 survey participants, a quarter (n = 56) were screened positive for severe PD. In multivariable analyses, age (1-year increment) (adjusted odds ratio = 0.94, 95% confidence interval [0.90, 0.98]) and a decrease in non-injection drug use versus no change (0.26 [0.07, 0.92]) were protective against severe PD, while positive associations were found for any alcohol use in the past 6 months (3.73 [1.42, 9.78]), increased food insecurity (2.88 [1.19, 6.93]) and both moving around between neighbourhoods more (8.71 [2.63, 28.88]) and less (3.03 [1.18, 7.74]) often compared to no change. This study documented a high prevalence of severe PD among PWUD during the COVID-19 pandemic compared with pre-COVID-19 data. Social determinants such as food insecurity and mobility issues, alongside demographic and substance use-related factors, were linked to distress. Evidence-based risk mitigation strategies for this population could reduce negative consequences in future pandemics or disruptions.

Transarterial microembolization for the management of refractory chronic joint pain in osteoarthritis.

Osteoarthritis (OA) is a chronic degenerative disease significantly impacting both patient quality of life and socioeconomics. Traditional treatment options, including pharmacological and surgical interventions, are often limited. Advancements in our understanding of the pathological mechanisms behind OA indicate the involvement of pathological angiogenesis. Transarterial microembolization (TAME), a minimally invasive radiological procedure, may present an innovative therapeutic approach. This review aims to consolidate current knowledge and experiences regarding TAME as a therapeutic modality for alleviating chronic joint pain associated with OA. It explores the role of TAME, focusing on its indications, patient selection, clinical outcomes, and future perspectives. Potential complications and associated risks are systematically addressed, alongside proposed strategies for risk mitigation and effective management.The presented patient cases originate from our institution, supplemented by a thorough review of relevant literature retrieved from PubMed.TAME represents a promising therapeutic approach, providing relief from the burden of joint diseases and substantially enhancing patient quality of life. Clinical outcomes emphasize the efficacy and safety of TAME in mitigating pain and improving functional capabilities in patients with chronic joint pain associated with OA. With mounting evidence of its therapeutic benefits and applicability to numerous joint-related pathologies, TAME offers a valuable addition to the arsenal of treatments for these conditions. · TAME is an innovative therapy for treating chronic joint pain related to OA.. · TAME is a technically challenging minimally invasive intervention requiring a high level of expertise.. · Understanding the challenges and complications of TAME can reduce risk and enhance procedural outcomes.. · Wilms LM, Jannusch K, Weiss D et al. Transarterial microembolization for the management of refractory chronic joint pain in osteoarthritis. Fortschr Röntgenstr 2024; DOI 10.1055/a-2288-5743.

Using Failure Mode and Effects Analysis to Decrease Risk When Developing a Home Health Nurse Residency Program.

New graduate nurses are traditionally not recruited for home health care (HHC). Due to staffing shortages, a HHC agency was interested in hiring graduate nurses, but there was concern about associated risks. The purpose of this quality improvement project was to develop a nurse residency program to safely transition graduate nurses to the HHC setting. After initial program design, analysis using a failure mode effects analysis (FMEA) was conducted, and risk mitigation strategies were applied. The overall risk of onboarding graduate nurses in HHC was reduced by 42% after applying harm reduction tactics identified from the FMEA. The FMEA was found to be a useful tool to prospectively identify areas of concern and apply harm reduction tactics prior to nurse residency implementation.

Advancements in Fire Safety Technology: A Automatic Fire Extinguishing Robot

Fire security which is a lasting and persistent issue requires the continuous development of technology to reinforce protection and inhibition of the fires which are regular event that often leads to emergencies in many sectors. This research endeavors to delve deeply into the realm of fire safety technology, focusing keenly on two innovative approaches: the Chai roof closers security policy, after house-closers protection process, and a matted fire extinguisher robot that notifies about the fire. Through scrupulously examining their technical detail, experiments, operational patterns, and potential outcomes and impact, the paper in this order to fulfills its objective of giving the reader a comprehensive and all-encompassing understanding of the extent and the take of these types of fire systems in the total field of fire safety. Relying on a broad review of journal articles, experiments, and also a proactive approach to these technological advancements that might contribute to averting fire disasters and on preserving both human lives and valuable properties, this research undertakes a mission to explore the emergence of these technological advancements and how much they instill change in the conduct of disaster response and risk mitigation.

Comparative effectiveness of C3-Hydrocarbons and their mixtures on suppression of hydrogen-air explosions

The next-generation energy economy relies heavily on hydrogen. Handling hydrogen during production, storage, and transportation requires risk mitigation. In this regard, the application of halide-free additives is very crucial. The present study experimentally and theoretically investigates the individual and combined inhibiting effect of propane and propene on hydrogen-air explosions. The experiments are carried out at 298 K and 1 bar, with additives ranging from 0.5 to 5% for the individual and 1–2% each for the combined effect in the 20–50% hydrogen-air mixture. Experimentally, the additive content in the stoichiometric and rich mixture decreases the explosion pressure, maximum pressure rise (MPR) rate, laminar burning velocity (LBV), and flame temperature, while increasing minimum ignition energy. For instance, the addition of 2% propane in a 50% hydrogen-air mixture, reduces the MPR rate by 98%, whereas such an effect is observed with 2.5% propene. Theoretical study shows that 3% equivalent additive augments inhibiting reactions, countering the main flame-promoting reaction R1 (H + O2O + OH) and thereby reducing LBV. Due to its low activation energy, propene consumes the active radicals twice the rate of propane, while propane not only scavenges the active radicals but also promotes oxygen consumption in the prior phase of combustion, weakening flame propagation. Overall, this approach exhibits a significant step forward in harnessing the potential of hydrogen while mitigating the safety risks associated with its use.

Seismic Monitoring of a Deep Geothermal Field in Munich (Germany) Using Borehole Distributed Acoustic Sensing

Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator’s cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system.

Dynamic Prediction Model of Financial Asset Volatility Based on Bidirectional Recurrent Neural Networks

Predicting financial market volatility is essential for investors and risk management. This study proposes a dynamic prediction model for financial asset volatility, with a Bi-directional Recurrent Neural Network (Bi-RNN) utilized to cleverly address market complexity. Our framework integrates Bi-RNN and gated recurrent units (GRU) to perform global optimization via particle swarm optimization algorithm (PSO). Bi-RNN combines historical data and future expectations, while GRU effectively solves long-term dependency issues through a gating mechanism, which enhances model generalization. Experimental results show that the model exhibits significant performance advantages on different financial datasets, along with strong learning and generalization capabilities superior to traditional methods. This research provides advanced and practical solutions for financial asset fluctuation prediction and is of positive significance for the greater accuracy of investment decisions and risk mitigation.

Experimental study on inhibition of coal spontaneous combustion by carbon dioxide and nitrogen

ABSTRACT Injecting CO2 and N2 to reduce the concentration of O2 in the air is a commonly used measure to prevent coal spontaneous combustion (CSC). To study the difference in inhibitory effects of CO2 and N2 on coal oxidation, experiments in this study were conducted using a setup of self-developed gas-bath coal oxidation for low temperatures. The oxidation process of the coal-oxygen system was then analyzed using reaction kinetics. Two different coal samples were tested in experiments under two inert gas environments, i.e. CO2 and N2 with various O2 concentrations, respectively. The changes in O2 and CO concentrations were measured during the oxidation process. By studying the standard oxygen consumption rate (SOCR) and the apparent activation energy of the coal-oxygen reaction system, inhibitory effects of CO2 and N2 on CSC were analyzed and compared. The results showed that the SOCR of the same coal sample remained consistent and independent of the O2 concentration within the same inert gas environment although under different O2 levels. In both CO2-O2 and N2-O2 environments, the SOCR of the coal sample exhibits exponential growth with temperature. The SOCR was found to have a functional connection with temperature, it can be represented as y = a ⋅ e bT , and the R 2 (determination coefficients) of the fitting formulas are all greater than 0.94. In the correlation, the inert gas environment has minimal impact on coefficient “b,” while in the CO2-O2 environment, reducing the value of coefficient “a” can effectively lower the coal sample’s SOCR. The GBMK sample and YMWK sample have an “a” value that is 20% of the value in the N2-O2 environment when oxidizing in the CO2-O2 environment. The SOCR in the CO2-O2 environment showed a decrement of between 26.0% and 52.1% when compared to that in the N2-O2 environment, whereas the activation energy showed an increment between 1.4% and 18.6%. The findings in this work provide an in-depth understanding of the prevention of CSC in the gob for risk mitigation.

Influence of climatic phenomena on rainfall in the Tapajós River Basin

Climate change results in weather phenomena such as those that are currently being observed, affecting precipitation, possibly more intensely in the Amazon region. This research is justified by the scarcity of climate risk mitigation measures in the Amazon. Rainfall data from 1992 to 2021 were analyzed, revealing long occurrences of dipole anomalies for consecutive months. Based on the climate indices, the best correlation results occurred in the years with high rainfall, coinciding with the occurrence of negative dipole and of La Niña. Therefore, climate indices can indicate the possible predictive capacity of extreme rainfall events. The results show that the influence of climatic phenomena is more pronounced in the northern region of the Tapajós River Basin.

Pediatric Facial Fractures: Demographics, Injury Patterns, and Associated Injuries in 3334 Patients.

Pediatric craniofacial fractures are fundamentally distinct from their adult counterparts because of unique injury patterns and effects on future growth. Understanding patterns and injury context informs management and risk mitigation. Previous studies include only inpatients, operative patients, or are specialty-specific. In contrast, our study presents a comprehensive assessment of all pediatric facial fracture patients seen at a single institution. Patients under 18 years old who were evaluated for facial fractures at a level I pediatric trauma center between 2006 and 2021 were reviewed. Subanalysis was performed for groups defined by age. Variables studied included demographics, etiology, fracture pattern, associated injuries, management, and outcomes. Three thousand thirty-four patients were included. Mean age at presentation was 11.5 to 4.9 years. The majority were Caucasian (82.6%) and male (68.4%). Sports were the leading cause of injury in older patients (42.2% of patients over 12y), compared with activities of daily living in patients under 6 years (45.5%). Thirty-two percent of patients were hospitalized, 6.0% required ICU care, and 48.4% required surgery. Frequency of ICU admission decreased with age (P<0.001), whereas operative intervention increased with age (P<0.001). Zygomaticomaxillary complex (P=0.002) and nasal fractures (P<0.001) were common in older patients, whereas younger patients experienced more skull (P<0.001) and orbital fractures (P<0.001). The most associated injuries were soft tissue (55.7%) and neurologic (23.6%). This large-scale study provides updated characterization of craniofacial fractures in the pediatric population, providing a necessary framework for future studies on outcomes assessments and preventative care.