Risk Analysis Research Articles

Using the data-augmented heterogeneous graph neural networks to identify risk of complex R&D projects considering project similarity

PurposeIdentifying potential risks is important in project risk management, especially in complex R&D projects that are commonly implemented as project portfolio (PP). However, most of the existing data-driven risk prediction and identification methods focus on individual projects and specific risks, and there is limited research on risk prediction and identification methods that also consider the similarity between risk propagation and projects. This study aims to provide a data-driven approach for risk identification in complex R&D projects, expanding the tools used for risk prediction.Design/methodology/approachThis study proposes a similarity measurement framework for R&D projects. A relational graph conventional network based on Node2vec, referred to as Node2vec-RGCN, is then utilized for data augmentation in the project risk association network, facilitating risk identification. Finally, the model is validated on real data.FindingsThe test results indicate that the average accuracy of our model is 70.2%, the call rate is 73.4% and the AUC indicator is 71.9%, which enables better identification of potential risks and analysis of their possible sources.Research limitations/implicationsIn this study, for the first time, link prediction based on graph neural networks is used in project risk networks to replace guesswork in risk identification by data-driven approach. However, there are still some limitations. The first limitation is that projects have a long life cycle, and risks may occur in different project phases. Therefore, to consider the dynamics of risks and project phases, the concept of time can be added to graph neural networks. The second limitation is that there can be complex interactions between different risks, and one risk can trigger the occurrence of other risks. However, in our study, risks were treated as independent and interactions between risks were not considered. In the future, risk identification can incorporate risk interactions to make the risk identification model more comprehensive.Practical implicationsSpecifically, it assists program managers in making decisions across three components: (1) During the project initiation phase, subsequent to the acquisition of data from the enterprise’s risk register and the project’s historical case base, it may, to a certain degree, supplant risk identification that relies on expert opinion, thereby facilitating the project in identifying potential risks and their fundamental characteristics. (2) During the project planning phase, it is also possible to conduct a risk assessment based on the risk scores generated through deep learning. This process enables the prioritization of risks and the allocation of risk response resources to address those of higher significance. (3) During the project execution phase, an updated list of potential risks and their prioritization may be generated by revising the model data according to newly generated dynamic risks. Finally, although our model is for R&D projects for risk prediction, except for the calculation of project similarity, which is mainly for R&D projects, all other data are standard features common to PP, so as long as the similarity calculation is done for projects in different industries for model adjustment, it can be applied to PP in various industries for risk identification and prediction, and it has strong transfer ability.Social implicationsThe contribution of this study is mainly in three aspects. First, our proposed model considers the risk propagation caused by the PIs. Most existing prediction studies for project risk identification and analysis ignore the impact of complex relationships between projects on risk prediction. Therefore, our model results make the decision-making more reliable and objective. Second, the model is an effective risk management tool based on ML that can assist managers in decision-making. This study uses a real data set from a company that can help project managers identify potential risks by using data-driven instead of guesswork with records of risks that have occurred in the past and project similarities.Originality/valueThis study develops a hybrid risk identification model that integrates project similarity, Node2vec and RGCN, applying graph neural networks to capture risk impacts from other projects in the risk propagation of project portfolios. The results of the study replace project decision-makers’ guesses about potential risks with a data-driven approach.

Environmental Requirements for Germination and Appressorium Formation of Ascospores and Conidia of Phyllosticta citricarpa, the Causal Agent of Citrus Black Spot

ABSTRACTPhyllosticta citricarpa produces ascospores and conidia that infect citrus tissues and cause citrus black spot (CBS). The environmental conditions impact the pre‐penetration process of these spores and the CBS intensity worldwide. However, there are few studies related to the optimal conditions for P. citricarpa spore germination, especially ascospores. Thus, this study aimed to assess the influence of temperature and wetness duration on germination of both P. citricarpa spores. Suspensions of ascospores and conidia produced by P. citricarpa were prepared, deposited on polystyrene dishes and kept at temperatures from 10°C to 40°C. Spore germination was assessed from 3 to 48 h of wetness. The beta‐monomolecular model was fitted to the data and response surface equations were generated for each spore type. Germination peaks of up to 62% and 90%, and appressorium formation of 61% and 80%, were found for ascospores and conidia, respectively, from 24 h of wetness onwards. Minimum, optimum and maximum temperatures were estimated as 10°C, 24.8°C and 40°C for conidial germination, and 11°C, 24.5°C and 38.4°C for appressorium formation, respectively. The cardinal temperatures estimated for ascospore germination were 9°C, 29.8°C and 39.8°C, and 10°C, 30°C and 39°C for appressorium formation. The differences found between both spores, notably in the optimal temperatures, may influence the role of each type of inoculum in CBS epidemics in the citrus‐growing areas worldwide. The response surface models generated in our study are a first step in the development of decision support systems for CBS management, as well as in the disease risk analyses.

Evaluation of Water Safety Plan Compliance in Italian Hospitals According to Legislative Decree 18/23 and Directive EU 2020/2184: A Cross-Sectional Study

In 2020, Directive (EU) 2020/2184 was published and subsequently transposed into Italian legislation via Legislative Decree 18/23 (Lgs.D. 18/23). The Directive aims to protect public health through a proactive approach based on a site-specific risk analysis along the entire water supply chain (Water Safety Plan, WSP). Between February and November 2024, a survey was conducted in Italy to assess both hospitals’ knowledge of Lgs.D. 18/23 and the application of the WSP in these facilities. A total of 300 hospitals were asked to complete an anonymous questionnaire containing 60 questions about the characteristics of the facility and the management of the water network, including the chemical–physical and microbiological monitoring of Legionella and other microbiological parameters. A total of 102 questionnaires were sent out (response rate: 34%), but only 72 were properly completed and analyzed. The results of the chemical–physical monitoring are not presented in this manuscript. Overall, 52.8% of the hospitals were built before 2000, and most are aware of Directive (EU) 2020/2184, Lgs.D.18/23 (80.6%), in particular, Article 9 on the risk assessment and management of internal water systems and the guidelines for its implementation (77.8%). All hospitals perform annual microbiological water testing, including Legionella analysis, with a detection rate of <50%. National guidelines for the implementation of WSPs are known in 75% of the hospitals, but only 38.9% have started planning to implement them, and 13.9% organize staff training on the subject. The questionnaire responses highlight the need to train hospital staff in water system risk management and WSP planning, which will be mandatory by 2029.

Effective Electrical and Thermal Conductivities of Porous Sintered Metallic Compacts

Abstract In this paper, five laws proposed to describe the influence of porosity on the effective conductivity (both electrical and thermal) of porous materials, which include a single microstructural parameter, are analyzed. These five laws are as follows: (1) the linear law with threshold, (2) the power law without threshold (Archie’s law), (3) the power law with threshold (percolation law), (4) the exponential law without threshold (decay law), and (5) the exponential law with threshold (Mooney-type law). These laws are discussed and derived from statistical reasoning (taken from the field of Stereology) or from probabilistic reasoning (taken from the field of risk analysis). The five laws are contrasted with the experimental values of electrical and thermal conductivities measured on metallic samples with different porosity, obtained by cold pressing and furnace sintering of four elementary powders (2 of iron, nickel, and aluminum). The study carried out allows to conclude that all the laws analyzed provide satisfactory agreements, but the percolation law is slightly superior to the others, since the values of its microstructural parameter remain within the uncertainty range to support a coherent physical interpretation, which is not the case for the other laws. Finally, the work also addresses the variability with porosity of the Wiedemann–Franz law, finding that it holds reasonably valid for low porosities, but not for higher porosities, which has been justified as a result of the different influence that the oxide layers of the metal powder particles exert on the electrical and thermal conductivities. Graphical Abstract

Metformin use and risk of colorectal cancer in patients with inflammatory bowel disease: a nationwide, Population-Based cohort study.

Patients with inflammatory bowel disease (IBD) have a significantly elevated risk of colorectal cancer (CRC). While metformin has been associated with lower CRC risk in general diabetic populations, its effect in patients with coexisting IBD and diabetes-a group at compounded risk-remains unclear. We conducted a nationwide cohort study using Taiwan's National Health Insurance Research Database linked to the Taiwan Cancer Registry. Among 241,590 adults newly diagnosed with IBD between 2008 and 2019. After applying strict eligibility criteria and 1:1 propensity score matching, 1,695 metformin users were matched to 1,695 non-metformin users. Adjusted hazard ratios (aHRs) for incident CRC and all-cause mortality were estimated using time-dependent Cox models and Fine-Gray competing risk analysis. Dose-response was assessed via restricted cubic splines. Metformin use was associated with a 56% lower risk of CRC (aHR, 0.44; 95% CI, 0.29-0.68), with similar findings in competing risk models (subdistribution HR, 0.49; 95% CI, 0.32-0.76). All-cause mortality was reduced by 32% (aHR, 0.68; 95% CI, 0.57-0.80). CRC risk decreased with higher cumulative exposure; those in the highest quartile had a 67% lower risk (aHR, 0.33; 95% CI, 0.19-0.57). The lowest CRC risk occurred at ∼0.4 defined daily dose/day (∼800 mg/day). Metformin use was associated with a lower risk of colorectal cancer in patients with IBD. These findings suggest a potential chemopreventive role, but confirmation from prospective randomized studies is needed before drawing causal inferences.

Frailty Indices in Patients Undergoing Functional Neurosurgical Procedures: A Systematic Review.

Functional neurosurgery covers a wide array of neurological disorders with an equally vast array of treatment modalities, including neuromodulation, decompressive, & ablative therapies for disparate pathologies such as pain, neuromodulation, disconnection, and refractory epilepsy. One of the most common functional treatments is deep brain stimulation for movement disorders and select psychiatric diseases. Functional neurosurgery treats patients with reduced quality of life from pathological neuronal pathways. Optimal patient selection by preoperatively identifying high risk patients is critical for avoiding as many operative complications as possible, in addition to managing complications better once they occur. Frailty indices have demonstrated superior discrimination in predicting adverse postoperative outcomes across the spectrum of neurosurgical subspecialties when compared to increasing patient age. This systematic review describes multiple different frailty indices utilized by patients undergoing functional neurosurgery procedures. A systematic review of literature was performed using PubMed. The Newcastle Ottawa Scale (NOS) was used to assess for risk of bias and studies with NOS > 6 were considered high quality. An initial search identified 541 articles through our search strategy and, after screening and review, five met criteria for inclusion The 5-factor modified frailty index (mFI-5) and Risk Analysis Index (RAI) were most frequently utilized (n = 5). One study utilized single-hospital databases in contrast to the nationwide databases utilized by the other four studies. RAI was found to have superior predictive ability as frailty metric when compared to the mFI-5. All five studies were considered high-quality based on the NOS. Frailty indices have demonstrated the ability to predict adverse outcomes in patients undergoing procedures from across the spectrum of neurosurgical subspecialties. Our review identified articles that utilized frailty indices in predicting outcomes among patients undergoing functional neurosurgery procedures.

Chronic kidney disease in isolated core antibody positive hepatitis B patients: Risk analysis from NHANES 2017-2020 study.

Chronic kidney disease in isolated core antibody positive hepatitis B patients: Risk analysis from NHANES 2017-2020 study.

Differences in Risk Analysis between Workers and Managers: Study from the Perspective of Neuroscience

Differences in Risk Analysis between Workers and Managers: Study from the Perspective of Neuroscience

Hydrogen Integration into Natural Gas Pipelines: Risk Analysis and Regulatory Recommendations

Hydrogen Integration into Natural Gas Pipelines: Risk Analysis and Regulatory Recommendations

Stuck in the sustainable mobility transition: A spatial analysis of transport poverty risk in the Netherlands

Stuck in the sustainable mobility transition: A spatial analysis of transport poverty risk in the Netherlands

An analysis of prognostic risk and immunotherapy response of glioblastoma patients based on single-cell landscape and nitrogen metabolism.

An analysis of prognostic risk and immunotherapy response of glioblastoma patients based on single-cell landscape and nitrogen metabolism.

Statistical analysis of motorcyclists' safety behavior and crash risks in Kuwait

Statistical analysis of motorcyclists' safety behavior and crash risks in Kuwait

Ensuring Safety and Optimization in Total Joint Arthroplasty of Cancer Patients: A Comprehensive Analysis of Complication Risks and Modifiable Risk Factors

Ensuring Safety and Optimization in Total Joint Arthroplasty of Cancer Patients: A Comprehensive Analysis of Complication Risks and Modifiable Risk Factors

Spatial and Temporal Analysis of Flood Risk in Massachusetts Environmental Justice Communities

Spatial and Temporal Analysis of Flood Risk in Massachusetts Environmental Justice Communities

Financial market risks and the hedging powers of unconventional assets under different conditions

ABSTRACT The global financial ecosystem has become increasingly precarious for investors in the face of diverse risks such as macroeconomic, policy uncertainty, geopolitical, and systemic risks. This study examines hedging these risks with alternative classes of unconventional assets; clean stocks, precious metals, Shariah-compliant stocks, and REITs, as contribution to the literature that contains fragmented analysis of individual assets or specific risks. The study employs a generalized least squares estimator that carefully eliminates salient econometric problems alongside quantile analysis using daily data spanning 5/17/2010 to 12/16/2024. The striking findings therefrom are: (i) precious metals, especially gold, are the best hedging candidates except against geopolitical risk where clean stocks come in to provide cover; (ii) analyses of quantiles provide fresh insights that indicate that most of the hedging powers of the assets are found during bearish market condition. The study accentuates the use of gold for portfolio diversification and for keeping foreign reserves.

The Potential Diagnostic Application of Artificial Intelligence in Breast Cancer.

Breast cancer poses a significant global health challenge, necessitating improved diagnostic and treatment strategies. This review explores the role of artificial intelligence (AI) in enhancing breast cancer pathology, emphasizing risk assessment, early detection, and analysis of histopathological and mammographic data. AI platforms show promise in predicting breast cancer risks and identifying tumors up to three years before clinical diagnosis. Deep learning techniques, particularly convolutional neural networks (CNNs), effectively classify cancer subtypes and grade tumor risk, achieving accuracy comparable to expert radiologists. Despite these advancements, challenges, such as the need for high-quality datasets and integration into clinical workflows, persist. Continued research on AI technologies is essential for advancing breast cancer detection and improving patient outcomes.

A Comparative Risk Analysis of Cone Beam Computed Tomography-based Daily Adaptive Radiation Therapy and Cone Beam Computed Tomography-based Radiation Therapy Alone.

A Comparative Risk Analysis of Cone Beam Computed Tomography-based Daily Adaptive Radiation Therapy and Cone Beam Computed Tomography-based Radiation Therapy Alone.

Emerging Risk Analysis Platform (ERAP)

Abstract This external scientific report documents the activities carried out by the scientific contractor within the Emerging Risk Analysis Platform (ERAP) project. It provides a consolidated and self‐contained overview of the platform's development, testing, deployment, and user support. The report consolidates insights gathered throughout the entire project duration, including feedback from users and lessons learned during all project phases. It also outlines identified areas for improvement and presents proposals for future enhancements – including AI‐related features. As such, the report provides a comprehensive overview of the ERAP project's progress and serves as a foundation for the platform's future development in alignment with the EFSA needs.

Assessing Climate Impact on Heritage Buildings in Trentino—South Tyrol with High-Resolution Projections

Climate variations impact the preservation of heritage buildings, necessitating a strategic understanding of potential effects to effectively guide preservation efforts. This study analyzes temperature- and precipitation-dependent climate-heritage indices in Trentino–South Tyrol using EURO-CORDEX regional climate models for the period 1971–2100 under RCP 4.5 and RCP 8.5 scenarios. The selected indices were calculated with climdex-kit and relied on bias-adjusted temperature and precipitation data with a 1 km spatial resolution. The obtained results indicate a geographically punctuated increase in biomass accumulation on horizontal surfaces, a slight decreasing trend in freeze–thaw events, an increase in growing degree days indicating a small, heightened insect activity, and a rise in heavy precipitation days. The Scheffer Index shows a significantly increased potential for wood degradation, particularly under the RCP 8.5 scenario, while the Wet-Frost Index remains consistently low. Finally, according to each identified hazard, adaptive solutions are suggested. These findings provide critical insights into future climate impacts on heritage buildings in the region, aiding stakeholders in planning targeted interventions. The study emphasizes the crucial role of integrating detailed climate data into heritage preservation strategies, advocating for the inclusion of future risk analysis in the “knowledge path” in order to enhance the resilience of buildings.

‘Standard KAMs’, ‘Canary KAMs’, and ‘Naughty KAMs’: A Typology of Key Audit Matters to Explain Their Value Relevance

This paper develops a framework for understanding how key audit matters (KAMs) can be factored into investors’ risk assessments. Detailed interviews with seasoned investors representing international and regional asset managers and owners confirm that the number and type of reported KAMs are not ‘priced’ directly. Nevertheless, KAMs can be a source of unbiased and complementary information for understanding the likelihood and impact of risks that affect an organization's financial position and performance. The paper responds to calls for more interpretive work on financial statement audits with a specific focus on an overlooked but key user of audit reports. Unlike earlier work based on inferential testing of archival data and experiments using proxies for financial capital providers, this study is the first primary account of how KAMs are understood and applied by investors. Findings are based on a relatively small sample of informed investors. Other stakeholders are not engaged. Nevertheless, the proposed framework makes an important contribution by outlining an alternative risk classification schematic that can be used by investors and other users of financial statements. The study bridges the technical assurance and risk management literature while providing empirical evidence that will widen the risk analysis and classification field.