Specific Risk Levels Research Articles

Update from 2010 (standard operating procedure): protocol for the 2024 British Society of Gastroenterology Guidelines on colorectal surveillance in inflammatory bowel disease

IntroductionThe evolving landscape of inflammatory bowel disease (IBD) necessitates refining colonoscopic surveillance guidelines. This study outlines methodology adopted by the British Society of Gastroenterology (BSG) Guideline Development Group (GDG) for...

Identification and validation of ventricular rhythmic risk subgroups in hypertrophic cardiomyopathy by clustering analysis including left ventricular strain analysis

Abstract Background The excess mortality in hypertrophic cardiomyopathy (HCM) patients is mainly attributed to the occurrence of ventricular arrhythmia (VA).The prediction of VA remains challenging and could be improved. Purpose The study aimed to characterize the VA risk profile in HCM patients through clustering analysis combining conventional parameters with information derived from left ventricular longitudinal strain analysis (LV-LS). Methods 434 HCM patients (65% men, mean age 56 years) were retrospectively included from two referral centers and followed longitudinally (mean duration 6 years). The validation cohort included 177 HCM patients from an other tertiary French center. Mechanical and temporal parameters were automatically extracted from the LV-LS segmental curves of each patient in addition to conventional clinical and imaging data. A total of 287 features were analyzed using a clustering approach (k-means). The first endpoint for VA included suspected SCD, aborted cardiac arrest, appropriate ICD therapy (AIT), sustained ventricular tachycardia (SVT) and non-sustained ventricular tachycardia (NSVT) during follow-up. Results From the derivation cohort, 4 clusters were identified with a higher rhythmic risk for clusters 1 and 4 (VA rates of 26%(28/108), 13%(13/97), 12%(14/120), and 31%(34/109) for cluster 1,2,3 and 4 respectively) (Figure 1). These 4 clusters differed mainly by the LV mechanics. Patients from cluster 4 had a severe and homogeneous decrease of myocardial deformation (global longitudinal strain (GLS) -11%, normal value ≥ -20%) associated with LV and left atrium (LA) remodeling (left atrial volume index (LAVI) 46.6 vs. 41.5 ml/m², p=0.04 and LVEF 59.7 vs. 66.3%, p < 0.001) and impaired exercise capacity (% predicted peak work 58.6 vs. 69.5%; p= 0.025). Patients from cluster 1 showed a marked deformation delay and temporal dispersion on the segmental analysis associated with a moderate decrease of the GLS (-15.3%; p <0.0001 for GLS comparison between clusters). Patients from clusters 2 and 3 had a small GLS decrease (GLS -17.2 and -16.3%, respectively) with a less severe phenotype for cluster 2 and older patients for cluster 3 (mean age 62.1 vs. 54.1 years; p < 0.0001). The clustering analysis in the validation population resulted in the creation of 4 clusters overlapping the derivation cohort with similar characteristics (Figure 2). Clusters 1’ and 4’ had a higher incidence of ventricular arrhythmia with a VA rate of 22%(5/23), 18%(16/88), 0%(0/17), and 20%(10/49) for clusters 1’,2’,3’ and 4’ respectively. Conclusion Machine-learning-based cluster analysis processing LV-LS parameters in HCM patients identified 4 clusters with specific LV-strain patterns and different rhythmic risk levels. In the future, the automatic extraction and analysis of LV strain parameters could help to improve the risk stratification for SCD in HCM patients.Clustering analysisExternal validation

Understanding climate risks to world cultural heritage: a systematic analysis and assessment framework for the case of Spain

Understanding the spatial distribution of world cultural heritage in its present-day geographical context is the foundation for the identification of and subsequent protection from key threats and vulnerabilities, particularly those arising from anthropogenic climate change. To address this challenge, we classified 45 Spanish world cultural heritage sites (WCHS) listed in the UNESCO register (as of 2023) according to type, entry date, and creation date. To establish a basis for a detailed analysis of the specific impact of climate change on the Spanish WCHS, a spatial cartographic database was developed showing the relationships between the WCHS and key geographical and climatic variables. We then used historical climate data, combined with a review of the impact mechanism of climate conditions on cultural heritage, to quantitatively evaluate the extent to which the WCHS in Spain are affected by local climate conditions from five aspects: freeze thaw cycle, thermal stress (thermoclastism), hydrodynamic scoring, corrosion, and biodegradation. Based on the above climate condition risks, we identified the five Spanish WCHS with the greatest potential climate condition risks, including Santiago de Compostela (Old Town), Pyrénées—Mont Perdu, the Roman Walls of Lugo, the Routes of Santiago de Compostela: Camino Francés and Routes of Northern Spain, and the Tower of Hercules. Additionally, based on different shared socioeconomic pathways (SSPs), we conducted a qualitative assessment of climate risk changes for WCHS in Spain under climate change. We found that the SSP1-2.6 scenario had the lowest climate risk, emphasizing the importance of achieving carbon neutrality for the protection of the WCHS. Our work translates historical climate conditions into specific climate risk levels for cultural heritage, providing data and theoretical support for effectively assessing the climate risks to Spanish WCHS.

MathEagle: Accurate prediction of drug-drug interaction events via multi-head attention and heterogeneous attribute graph learning

BackgroundDrug-drug interaction events influence the effectiveness of drug combinations and can lead to unexpected side effects or exacerbate underlying diseases, jeopardizing patient prognosis. Most existing methods are restricted to predicting whether two drugs interact or the type of drug-drug interactions, while very few studies endeavor to predict the specific risk levels of side effects of drug combinations. MethodsIn this study, we propose MathEagle, a novel approach to predict accurate risk levels of drug combinations based on multi-head attention and heterogeneous attribute graph learning. Initially, we model drugs and three distinct risk levels between drugs as a heterogeneous information graph. Subsequently, behavioral and chemical structure features of drugs are utilized by message passing neural networks and graph embedding algorithms, respectively. Ultimately, MathEagle employs heterogeneous graph convolution and multi-head attention mechanisms to learn efficient latent representations of drug nodes and estimates the risk levels of pairwise drugs in an end-to-end manner. ResultsTo assess the effectiveness and robustness of the model, five-fold cross-validation, ablation experiments, and case studies were conducted. MathEagle achieved an accuracy of 85.85 % and an AUC of 0.9701 on the drug risk level prediction task and is superior to all comparative models. The MathEagle predictor is freely accessible at http://120.77.11.78/MathEagle/. ConclusionsThe experimental results indicate that MathEagle can function as an effective tool for predicting accurate risk of drug combinations, aiding in guiding clinical medication, and enhancing patient outcomes.

Risk profiles for ventricular arrhythmias in hypertrophic cardiomyopathy through clustering analysis including left ventricular strain.

AimsThe prediction of ventricular arrhythmia (VA) in hypertrophic cardiomyopathy (HCM) remains challenging. We sought to characterize the VA risk profile in HCM patients through clustering analysis combining clinical and conventional imaging parameters with information derived from left ventricular longitudinal strain analysis (LV-LS). MethodsA total of 434 HCM patients (65% men, mean age 56 years) were included from two referral centers and followed longitudinally (mean duration 6 years). Mechanical and temporal parameters were automatically extracted from the LV-LS segmental curves of each patient in addition to conventional clinical and imaging data. A total of 287 features were analyzed using a clustering approach (k-means). The principal endpoint was VA. Results4 clusters were identified with a higher rhythmic risk for clusters 1 and 4 (VA rates of 26%(28/108), 13%(13/97), 12%(14/120), and 31%(34/109) for cluster 1,2,3 and 4 respectively). These 4 clusters differed mainly by LV-mechanics with a severe and homogeneous decrease of myocardial deformation for cluster 4, a small decrease for clusters 2 and 3 and a marked deformation delay and temporal dispersion for cluster 1 associated with a moderate decrease of the GLS (p < 0.0001 for GLS comparison between clusters). Patients from cluster 4 had the most severe phenotype (mean LV mass index 123 vs. 112 g/m2; p = 0.0003) with LV and left atrium (LA) remodeling (LA-volume index (LAVI) 46.6 vs. 41.5 ml/m2, p = 0.04 and LVEF 59.7 vs. 66.3%, p < 0.001) and impaired exercise capacity (% predicted peak VO2 58.6 vs. 69.5%; p = 0.025). ConclusionProcessing LV-LS parameters in HCM patients 4 clusters with specific LV-strain patterns and different rhythmic risk levels are identified. Automatic extraction and analysis of LV strain parameters improves the risk stratification for VA in HCM patients.

Variation in benefit among patients with serious mental illness who receive integrated psychiatric and primary care.

The population with serious mental illness has high risk for hospitalization or death due to unhealthy behaviors and inadequate medical care, though the level of risk varies substantially. Programs that integrate medical and psychiatric services improve outcomes but are challenging to implement and access is limited. It would be useful to know whether benefits are confined to patients with specific levels of risk. In a population with serious mental illness and increased risk for hospitalization or death, a specialized medical home integrated services and improved treatment and outcomes. Treatment quality, chronic illness care, care experience, symptoms, and quality of life were assessed for a median of 385 days. Analyses examine whether improvements varied by baseline level of patient risk. Patients with greater risk were more likely to be older, more cognitively impaired, and have worse mental health. Integrated services increased appropriate screening for body mass index, lipids, and glucose, but increases did not differ significantly by level of risk. Integrated services also improved chronic illness care, care experience, mental health-related quality of life, and psychotic symptoms. There were also no significant differences by risk level. There were benefits from integration of primary care and psychiatric care at all levels of increased risk, including those with extremely high risk above the 95th percentile. When developing integrated care programs, patients should be considered at all levels of risk, not only those who are the healthiest.

Spatial Analysis on the Spread of Dengue Hemorrhagic Fever in Baubau, Southeast Sulawesi, Indonesia

We studied the spatial patterns of Dengue Hemorrhagic Fever (DHF) transmission in Baubau, a city in Southeast Sulawesi, Indonesia. DHF is a serious disease caused by the dengue virus and spread by Aedes mosquitoes. We used Moran’s Index, a spatial analysis tool, to create a DHF spread map for Baubau’s sub-districts. We found different patterns of DHF risk, such as: cold spots, Betoambari and Batupoaro had lower DHF cases, but they were vulnerable to infection from nearby areas; hot spot, Murhum had higher DHF cases and could transmit the disease to neighboring areas; and low risk, Bungi had the lowest DHF risk and was resilient to infection. Our findings suggest that preventive measures should be tailored to the specific risk level of each sub-district. Our study also provides useful guidance for controlling DHF transmission in Baubau and beyond. Our research is a beacon of hope for a safer and healthier future.

Identification of novel risk profiles for ventricular arrythmias in hypertrophic cardiomyopathy through clustering analysis including left ventricular strain data

Abstract Aims The excess mortality in hypertrophic cardiomyopathy (HCM) patients is mainly attributed to the occurrence of ventricular arrythmias (VA) and sudden cardiac death (SCD). The prediction of VA remains challenging and could be improved. The aim of the study was to characterize VA risk profile in HCM patients through clustering analysis combining clinical and conventional imaging parameters with information derived from left ventricular longitudinal strain analysis (LV-LS). Methods A total of 434 HCM patients (65% men, mean age 56 years) were retrospectively included from two referral centers from two different countries and followed longitudinally (mean duration 6 years). Mechanical and temporal parameters were automatically extracted from the LV-LS segmental curves of each patient in addition to conventional clinical and imaging data. A total of 287 features were analyzed using a clustering approach (k-means). The first endpoint for ventricular arrythmias (VA) included suspected SCD, aborted cardiac arrest, appropriate ICD therapy (AIT), sustained ventricular tachycardia (SVT) and non-sustained ventricular tachycardia (NSVT) during follow-up. Results 4 clusters were identified with a higher rhythmic risk for cluster 1 and 4 (VA rates of 26%(28/108), 13%(13/97), 12%(14/120), and 31%(34/109) for cluster 1,2,3 and 4 respectively) (Figure 1). These 4 clusters have been mainly separated based on LV strain parameters with severe and homogeneous decrease of myocardial deformation for cluster 4 (global longitudinal strain (GLS) -11%), a small decrease for cluster 2 and 3 (GLS -17.2 and -16.3%, respectively) and a marked mechanical and temporal dispersion for cluster 1 associated with a moderate decrease of the GLS (-15.3%; p &amp;lt;0.0001 for GLS comparison between clusters) (Figure 2). Patients from cluster 4 had the most severe phenotype (mean LV mass index 123 vs. 112 g/m²; p=0.0003) with marked diastolic dysfunction (left atrial volume index (LAVI) 46.6 vs. 41.5 ml/m² for others clusters; p=0.04) and impaired exercise capacity (% predicted peak work 58.6 vs. 69.5%; p= 0.025). Patient from cluster 2 had the less severe phenotype (mean LV mass index 99.6 vs. 121 g/m²; p=0.0001 and LAVI 37.2 vs. 44.3 ml/m²; p=0.04). Patients from cluster 3 and 1 had a moderate phenotype and were older in cluster 3 (mean age 62.1 vs. 54.1 years; p &amp;lt; 0.0001) and had more obstructive form in cluster 1 (peak gradient LV outflow tract ≥ 30 mmHg for 36 vs. 23% of patients; p=0.046) with a larger left atrium size (LAVI 45.8 vs. 41.7 ml/m²; p=0.0023). Conclusion Using a clustering approach processing LV-LS parameters in HCM patients identified 4 clusters with specific LV-strain patterns and different rhythmic risk level. In the future, the automatic extraction and analysis of LV strain parameters could help to improve the risk stratification for SCD in HCM patients.Figure 1:Survival curvesFigure 2:Different strain patterns

A machine learning-based Bayesian model for predicting the duration of ship detention in PSC inspection

Port state control (PSC) inspections are deemed as an effective way to detect substandard vessels and ensure maritime safety around the world. Despite great efforts on PSC in recent years, one challenge that still exists in today’s PSC inspection practice is that there lack relevant scheme or academic research focusing on the duration of vessel detention, which is of great importance to the inspection system. To assist port authorities in estimating detention duration and minimizing the existence of substandard vessels, this paper aims to develop a novel data-driven machine learning based model based on the inspection records collected within the jurisdiction of Paris MoU from January 2015 to March 2022. The model is trained via the incorporation of an Improved Tree Augmented Naïve (ITAN) learning approach and a maximum a posteriori probability (MAP) of Expectation Maximization (EM) approach for the first time within the context of PSC research, which could be used as a prediction tool to determine rational durations for detained vessels. Thorough analysis of the proposed model enables the identification of risk variables and deficiency types having significant effects leading to long duration of detention. Further, the research findings could reveal managerial suggestions and insights for port authorities to reduce the occurrence of substandard vessels via the inspection system, i.e., identify specific risk level of vessels and ensure a more-efficient vessel selection process; design specific instructions and rules to regulate risk variables and deficiencies with huge effect on a long duration of detention. This research will provide insightful reference for effectively improving vessel quality, inspection efficiency, and maritime safety.

A Multilevel Primary Care Intervention to Improve Follow-Up of Overdue Abnormal Cancer Screening Test Results

Realizing the benefits of cancer screening requires testing of eligible individuals and processes to ensure follow-up of abnormal results. To test interventions to improve timely follow-up of overdue abnormal breast, cervical, colorectal, and lung cancer screening results. Pragmatic, cluster randomized clinical trial conducted at 44 primary care practices within 3 health networks in the US enrolling patients with at least 1 abnormal cancer screening test result not yet followed up between August 24, 2020, and December 13, 2021. Automated algorithms developed using data from electronic health records (EHRs) recommended follow-up actions and times for abnormal screening results. Primary care practices were randomized in a 1:1:1:1 ratio to (1) usual care, (2) EHR reminders, (3) EHR reminders and outreach (a patient letter was sent at week 2 and a phone call at week 4), or (4) EHR reminders, outreach, and navigation (a patient letter was sent at week 2 and a navigator outreach phone call at week 4). Patients, physicians, and practices were unblinded to treatment assignment. The primary outcome was completion of recommended follow-up within 120 days of study enrollment. The secondary outcomes included completion of recommended follow-up within 240 days of enrollment and completion of recommended follow-up within 120 days and 240 days for specific cancer types and levels of risk. Among 11 980 patients (median age, 60 years [IQR, 52-69 years]; 64.8% were women; 83.3% were White; and 15.4% were insured through Medicaid) with an abnormal cancer screening test result for colorectal cancer (8245 patients [69%]), cervical cancer (2596 patients [22%]), breast cancer (1005 patients [8%]), or lung cancer (134 patients [1%]) and abnormal test results categorized as low risk (6082 patients [51%]), medium risk (3712 patients [31%]), or high risk (2186 patients [18%]), the adjusted proportion who completed recommended follow-up within 120 days was 31.4% in the EHR reminders, outreach, and navigation group (n = 3455), 31.0% in the EHR reminders and outreach group (n = 2569), 22.7% in the EHR reminders group (n = 3254), and 22.9% in the usual care group (n = 2702) (adjusted absolute difference for comparison of EHR reminders, outreach, and navigation group vs usual care, 8.5% [95% CI, 4.8%-12.0%], P < .001). The secondary outcomes showed similar results for completion of recommended follow-up within 240 days and by subgroups for cancer type and level of risk for the abnormal screening result. A multilevel primary care intervention that included EHR reminders and patient outreach with or without patient navigation improved timely follow-up of overdue abnormal cancer screening test results for breast, cervical, colorectal, and lung cancer. ClinicalTrials.gov Identifier: NCT03979495.

Cost-effectiveness and potential budget impact of non-pharmacological interventions for early management in prehypertensive people: an economic evaluation for China

BackgroundNon-pharmacological interventions (NPIs) could be considered in the early management of prehypertensive population. This study aimed to evaluate the potential cost-effectiveness of NPIs and the budget impact of implementing NPIs on prehypertensive population in China and provide evidence of chronic disease management innovation for decision-makers.MethodsFive NPIs including usual care, lifestyle, strengthen exercise, relaxation, and diet therapy were selected based on the practice of hypertension management in China. A nine-state Markov model was constructed to evaluate the lifetime costs and health outcomes of five NPIs and a non-intervention group from the perspective of Chinese healthcare system. The effectiveness of NPIs was obtained from a published study. Parameters including transition probabilities, costs and utilities were extracted or calculated from published literature and open-access databases. Sensitivity analyses were conducted to test the uncertainty of all parameters. The impact of duration of intervention was considered in scenario analyses. A budget impact analysis (BIA) was conducted to evaluate the total cost and the medical cost saving of a hypothetical nationwide implementation of potential cost-effective NPI in prehypertensive people. Management strategies including focusing on patients with specific ages or different CVE risk levels, and different duration of implementation were taken into consideration.ResultsStrengthen exercise was the most cost-effective intervention with a probability of 78.1% under the given WTP threshold. Our results were sensitive to the cost of interventions, and the utility of prehypertension and hypertension. The duration of implementation had limited impact on the results. BIA results showed that the program cost was hefty and far more than the medical cost saving with the course of simulation time. Applying management strategies which focused on individual characteristics could largely reduce the program cost despite it remained higher than medical cost saving.ConclusionsStrengthen exercise was a potential NPI that can be considered in priority for early management in prehypertensive population. Although early management can acquire medical cost saving, the related program cost can be quite hefty. Precise strategies which may help reduce the cost of early management should be taken into consideration in program design.

Regional‐risk‐targeted seismic design: A novel approach for earthquake resistant design

AbstractCurrent seismic design codes define safety levels based on the seismic risk of individual structures, such as specifying a maximum probability of collapse in 50 years. The design remains the same whether the structure is located within a sparsely populated area or within a densely populated area provided that both areas have the same seismic hazard. A novel seismic design approach, referred to as regional‐risk‐targeted seismic design is proposed in which the design level is based on a target seismic risk at the regional scale, as opposed to at the individual structure level. The proposed approach, which is aimed at new constructions, allows local governments to define acceptable levels of regional seismic risk and modify the seismic design criteria contained in national standards to satisfy them. As the possible consequences of a seismic event increase at the regional level, the design level becomes more stringent to control the regional seismic risk to acceptable limits. A series of simplifications are considered to facilitate its application. We illustrate the application of the proposed regional‐risk‐targeted seismic design with three examples in the city of San Francisco, California. In these examples, we iteratively compute the modifications to the maximum seismic risk on individual structures required to comply with specific target levels of regional seismic risk. In the proposed approach, the maximum tolerable seismic risk in individual structures depends not only on the seismic hazard at the site, but also on the number of structures and density in the region of interest. It is concluded that the proposed seismic design approach provides a more rational tool for local government officials to set and calibrate seismic design criteria to comply with their locally‐defined acceptable levels of regional seismic risks within a probabilistic risk management framework.

Risk Management of Supply Chain Green Finance Based on Sustainable Ecological Environment

Green supply chain finance is a new financing method that focuses on corporate restructuring and promotes corporate capital flow and the development of environmental protection. This paper used BP neural network technology to study the green financing of the supply chain under the sustainable ecological environment. The method played an important role in the trial. Due to the more uncertain factors faced and the more complex environment, the risks of green supply chain finance are more hidden, diverse, and complex. The BP neural network is relatively mature in both network theory and performance. Its outstanding advantages are its strong nonlinear mapping ability and flexible network structure. The positive effect of BP neural network on green financial risk management is verified by experiments. Green supply chain finance is an innovative model of green finance. This experiment studies the risk management of green finance in supply chain and the evaluation index of green finance risk management through BP neural network method, and shows that the evaluation results are highly scientific. In addition, based on the green supply chain model, the historical data of different regions provide a scientific basis for the sustainable ecological development of the region. This paper provides guidance for the sustainable development of green finance in the supply chain and makes contributions to promoting the development of green economy. In order to control the risks of supply chain financing business, the risks of supply chain financing business are classified and analyzed, and specific project risk levels and points are determined to propose control measures to ensure effective control of the business risks.

Perinatal outcomes in nulliparous women with a history of multiple pregnancy losses according to number of previous pregnancy losses

While it is widely acknowledged that pregnancy losses can lead to negative outcomes for both mothers and fetuses, there is limited information available on the specific levels of risk associated with each additional pregnancy loss. This study aimed to investigate the effect of number of previous pregnancy losses among nulliparous women on maternal and neonatal adverse outcomes. This was a multicenter retrospective cohort study. The study population included all nulliparous women with singleton pregnancies who delivered in all university-affiliated obstetrical centers in a single geographic area between 2003 and 2021. Maternal and neonatal outcomes of women who delivered at our medical centers and had varying numbers of previous pregnancy losses were compared with women who had no previous pregnancy loss. The primary outcome of this study was preterm delivery rate at <37 weeks of gestation. The secondary outcomes were adverse maternal and neonatal outcomes. Univariate analysis was performed using multiple logistic regression modeling. During the study period, 97,904 nulliparous women met the inclusion and exclusion criteria. Of those women, 84,245 (86%) had no previous pregnancy losses (reference group), 10,724 (11%) had 1 previous pregnancy loss, 2150 (2.2%) had 2 previous pregnancy losses, 516 (0.5%) had 3 previous pregnancy losses, 160 (0.2%) had 4 previous pregnancy losses, and 99 (0.1%) had ≥5 previous pregnancy losses. Women who had previous pregnancy losses had significantly higher rates of preterm delivery, hypertensive disorders of pregnancy, diabetes mellitus (pregestational and gestational), unplanned cesarean delivery, perinatal death, neonatal intensive care unit admissions, and neonatal hypoglycemia. The risks of preterm delivery and most other adverse obstetrical outcomes correlated with the number of previous pregnancy losses. Multivariate analyses showed that each previous pregnancy loss was associated with an additional, significant, increased risk of preterm delivery of 14% at <37 weeks of gestation, 37% at <34 weeks of gestation, 45% at <32 weeks of gestation, and 77% at <28 weeks of gestation. A history of previous pregnancy losses increased the risk of preterm delivery and other perinatal outcomes in a dose-dependent manner. To minimize perinatal complications, obstetricians should be aware of the risks and complications in this unique population, consider close monitoring of the cervical length, and maintain high vigilance in case of complications with special attention to other potentially modifiable risks.

Combining Statistical, Displacement and Damage Analyses to Study Slow-Moving Landslides Interacting with Roads: Two Case Studies in Southern Italy

Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks.

Portfolio Optimization Analysis in American Industry

Portfolio optimization, as a process of reorganizing investment and dispersing risks by considering the established target return and risk tolerance, is widely used in the financial field. This paper analyzes the asset allocation of six kinds of assets. It uses three methods, namely, mean-variance analysis, CAPM, and the FF3F model, to optimize the investment portfolio. At the same time, the proportion of assets in the portfolio should be considered under the conditions of maximizing the return or minimizing the risk at a specific risk level. The results show that in the mean-variance model, the AEP and Gold ETF have the most considerable weight in the combination of maximum Sharpe ratio and minimum variance, respectively. In the FF3F model, AT&amp;T and Gold ETF have the most prominent weight in the combination of maximum Sharpe ratio and minimum variance, while in CAPM, Google and Gold ETF have the most significant weight in the combination of maximum Sharpe ratio and minimum variance respectively. This research may help potential investors interested in power, communication, science and technology, gold, and automobile industries.

Risk assessment for the native anurans from an alien invasive species, American bullfrogs (Lithobates catesbeianus), in South Korea

The invasive species are of global concern, and the Invasive American Bullfrog (IAB; Lithobates catesbeianus) is one of the worst invasive amphibian species worldwide. Like other countries, South Korea is also facing challenges from IAB. Although many studies indicated impacts of IAB on native anurans in Korea, the actual risk at the specific level is yet to evaluate. Considering the putative invasiveness of IAB, it is hypothesized that any species with the possibility of physical contact or habitat sharing with them, will have a potential risk. Thus, we estimated and observed their home range, preferred habitats, morphology, behavior, and ecology. Then, comparing with existing knowledge, we assessed risks to the native anurans. We found a home range of 3474.2 ± 5872.5 m2 and identified three types of habitats for IAB. The analyses showed at least 84% of native anurans (frogs and toads) were at moderate to extreme risks, which included all frogs but only 33% of toads. Finally, we recommended immediate actions to conserve the native anurans based on our results. As this study is the first initiative to assess the specific risk level from the invasiveness of L. catesbeianus, it will help the managers to set conservation priorities and strategies.

A multi-objective mathematical model for selecting reliable suppliers for one-shot systems

A One-shot device is defined as a one-time unit that cannot be subjected to destructive tests or used more than once. Each component/sub-component used in one-shot systems, regardless of how it is supplied, has a specific risk level. On the other hand, each component affects the factors influencing the total system failure. It is the main objective of the present paper to provide a multi-objective model for determining suppliers of components used in one-shot systems. In this model, the risk associated with each supplier is initially estimated using risk assessment indicators. In the second stage, the risks associated with sub-components are assess using cause-and-effect matrices. Based on the likely catastrophic incidents to the system, the fault tree analysis is used and the relevant diagrams are drawn to identify the best composition of equipment suppliers. The results obtained from a case-study are then used in the especially developed multi-objective model to identify the allocations. The optimum values for the first and second objective functions are calculated to be 1489.349 and 0.809, respectively. Finally, the performance of the model is validated by sensitivity analysis.

A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data scenarios

The reliability of an integrated energy system (IES) is most likely menaced by its weakest spots, and hence the prediction-based maintenance (PBM) is deployed to forecast these risky periods and locations where inspection and maintenance (I&M) actions are requisite. The key to accomplishing this is the pinpoint vulnerabilities prediction with sufficient lead time to prepare the PBM. With such motivations, one short-term prediction methodology for the spatiotemporal distribution of vulnerabilities, the fuzzy association pattern recognition covering rare and dependent factors (FAPRrdf), is developed in this paper. In the first preprocessing stage, a parallel learning process is formed. The discrete and continuous features are assessed through two expert models, respectively, to differentiate the common and rare components in each feature; Secondly, in the qualitative analysis, a two-expert-systems-integrated single entity approach is established to distinguish the “risky” components. The separated common and rare components are further evaluated to extract the high-impact (HI) and high-impact-low-probability (HILP) components, respectively. Ergo, the imbalanced data distribution can be solved, and the optimization step of weights between two independent expert models in some ensemble methods is no longer required; The third step is the quantitative analysis, and a structure importance measure (SIM)-based impact weight evaluation framework is proposed to rate the specific risk level of the “risky” components. The impact of each component on the variation of total system risks, as well as all the potential paths that will lead to a fault event in this system, are incorporated. Thereof, both the self-impacts of each component and the dependence on other components can be taken into account. Finally, this methodology is validated via an empirical case study, and its flexibility and feasibility during real applications can therefore be demonstrated.

Establishment of risk quality control charts based on risk management strategies.

Risk management strategies have been proposed for applications in clinical laboratories to reduce patient risks; however, effective and visual risk-monitoring tools are currently lacking in medical laboratories. In this study, we constructed a risk quality control (QC) chart based on risk management strategies. We calculated the risk levels of QC materials based on Bayes' theorem by combining the total allowable error, QC results, and the maximum number of unacceptable errors in the laboratory. Then, we constructed a risk QC chart by presenting the Z values and corresponding risk levels of QC materials simultaneously. Finally, we evaluated the risk-monitoring capabilities of the risk QC charts by simulating different long-term errors in the laboratory. The risk levels of QC materials increased as the QC results moved further away from the set mean. Larger sigma values led to fewer risks obtained for the same QC results. The constructed risk QC charts intuitively showed specific risk levels and could warn lab staff out-of-control, without the need for QC rules to make judgments. The risk levels of erroneous results differed for items with different sigma performance. Risk-based QC charts allowed visualization of the QC results and specific risk levels simultaneously, providing more intuitive results than those obtained from traditional QC charts.