Probabilistic Risk Research Articles

Spatiotemporal variations, source identification, and risk assessment of potentially toxic elements in the surface water of Felent Stream impacted by the silver mine.

The silver deposits located in the upper basin of the Felent Stream are currently the largest producing mine in the Türkiye. It is also significantly impacted by industrial, agricultural, and thermal spring-related waste in Kütahya Province. The main objectives of this study were to examine the spatiotemporal variations of 12 dissolved potentially toxic elements (PTEs) in the surface water of Felent Stream, to identify their possible sources, and to assess their probable risks. As a result of this study, among investigated PTEs, the highest mean concentrations of 3592-14,388µg/L for Mg and the lowest of 0.15-0.19µg/L for Cd were noted in Felent Stream water. The average concentrations of PTEs were found in the order of Mg > Ca > Na > As > Mn > B > Zn > Ni > Cu > Pb > Cr > Cd. Remarkably, during the dry season, there was a conspicuous escalation in the average PTEs contents of water, with an approximately multifold amplification. PTEs in stream water were evaluated for their potential ecotoxicological risks and possible sources. Based on ecological risk assessment indices, the stream exhibited low pollution levels during the wet season but displayed elevated pollution levels during the dry season, indicating a general shift towards heightened pollution conditions. The hazard index (HI) data for As exhibited significant potential noncarcinogenic risks across all monitoring stations. Conversely, the carcinogenic risk (CR) data underscored the imperative nature of addressing the health risks associated with As in the waters of the studied region. Mining activities were identified as the primary origin of PTEs based on principal component analysis (PCA). Moreover, upstream regions, proximal to the mining site, emerged as the most heavily contaminated areas according to cluster analysis (CA).

Quantitative assessment rules and models for dynamic disaster risk in high-density gas gathering stations: Practical application in a largest CBM gathering station

Coalbed Methane (CBM) gathering stations involve multiple engineering disciplines, with a concentration of pressure vessels and strong production continuity. Once accidents occur, they can easily result in severe casualties, property losses, and environmental pollution. Therefore, prior to the large-scale promotion and urban application of CBM, this paper proposes a dynamic quantitative risk assessment model for the operational and task-related risks in CBM gathering stations. Firstly, various methods are employed for risk factor identification. Then, a dynamic Bayesian network (DBN) model is constructed to assess the dynamic operational risks and task-related risks of CBM gathering stations under different maintenance conditions. This model provides the initial probability of operational risk occurrence at stations, predicts changes in the probability of equipment multi-state operational risks under different maintenance conditions over 10 years, and forecasts the probability of task-related risks during operations. Finally, a combined weighting model is established, incorporating instance information to comprehensively evaluate safety management and derive safety management correction coefficients. These coefficients are integrated with the probabilities of station operation and task-related risk occurrence to ultimately determine the likelihood of operational risk occurrence during station production, completing the dynamic quantitative assessment of operational risks in CBM gathering stations.

A Bayesian network analysis of the probabilistic relationships between metabolically healthy obesity and cardiovascular disease risk under new diagnostic criteria.

The relationship between metabolically healthy obesity (MHO) and cardiovascular disease (CVD) risk remains debated. The critical point may be the lack of consensus on MHO's definition and diagnostic criteria. This study aimed to investigate the association of MHO status with arteriosclerosis-CVD (ASCVD) risk in Chinese under new diagnostic criteria. Participants aged 35-79 in the 2009 China Health and Nutrition Survey cohort were included. The 10-year ASCVD risk was predicted by the prediction for ASCVD risk in China, and participants with a predicted risk of ⩾ 10% were classified into the high-risk group. The Bayesian network (BN) models were constructed to characterize the multivariable probabilistic connections between metabolically obesity phenotypes and ASCVD risk. The 10-year ASCVD risk score and the proportion of individuals at ASCVD high risk were significantly different between metabolically obesity phenotypes (P< 0.001). BN reasoning results showed that MHO individuals were not significantly associated with a 10-year ASCVD risk. Among metabolically unhealthy individuals, the conditional probability of high ASCVD risk increased with the Body Mass Index (BMI), with the conditional probability of high ASCVD risk was 24.63% (95% CI: 22.81-26.55%), 32.97% (95% CI: 30.75-35.27%) and 40.2% (95% CI: 36.64-43.86%) for metabolically unhealthy normal weight (MUNW), metabolically healthy overweight weight (MHOW), and metabolically unhealthy obesity (MUO) group, respectively. Subgroup analysis showed that MHO individuals were at increased risk of CVD compared with metabolically healthy normal weight (MHNW) individuals only in females. These results showed that there was no significant increase in ASCVD risk of MHO phenotype based on the new diagnostic criteria, suggesting that MHO is in a relatively healthy state.

Inflammatory signature-based theranostics for acute lung injury in acute type A aortic dissection.

Acute lung injury (ALI) is a serious adverse event in the management of acute type A aortic dissection (ATAAD). Using a large-scale cohort, we applied artificial intelligence-driven approach to stratify patients with different outcomes and treatment responses. A total of 2,499 patients from China 5A study database (2016-2022) from 10 cardiovascular centers were divided into 70% for derivation cohort and 30% for validation cohort, in which extreme gradient boosting algorithm was used to develop ALI risk model. Logistic regression was used to assess the risk under anti-inflammatory strategies in different risk probability. Eight top features of importance (leukocyte, platelet, hemoglobin, base excess, age, creatinine, glucose, and left ventricular end-diastolic dimension) were used to develop and validate an ALI risk model, with adequate discrimination ability regarding area under the receiver operating characteristic curve of 0.844 and 0.799 in the derivation and validation cohort, respectively. By the individualized treatment effect prediction, ulinastatin use was significantly associated with significantly lower risk of developing ALI (odds ratio [OR] 0.623 [95% CI 0.456, 0.851]; P = 0.003) in patients with a predicted ALI risk of 32.5-73.0%, rather than in pooled patients with a risk of <32.5 and >73.0% (OR 0.929 [0.682, 1.267], P = 0.642) (Pinteraction = 0.075). An artificial intelligence-driven risk stratification of ALI following ATAAD surgery were developed and validated, and subgroup analysis showed the heterogeneity of anti-inflammatory pharmacotherapy, which suggested individualized anti-inflammatory strategies in different risk probability of ALI.

Prediction of disease-free survival using strain elastography and diffuse optical tomography in patients with T1 breast cancer: a 10-year follow-up study

BackgroundEarly-stage breast cancer (BC) presents a certain risk of recurrence, leading to variable prognoses and complicating individualized management. Yet, preoperative noninvasive tools for accurate prediction of disease-free survival (DFS) are lacking. This study assessed the potential of strain elastography (SE) and diffuse optical tomography (DOT) for non-invasive preoperative prediction of recurrence in T1 BC and developed a prediction model for estimating the probability of DFS.MethodsA total of 565 eligible patients with T1 invasive BC were enrolled prospectively and followed to investigate the recurrence. The associations between imaging features and DFS were evaluated and a best-prediction model for DFS was developed and validated.ResultsDuring the median follow-up period of 10.8 years, 77 patients (13.6%) developed recurrences. The fully adjusted Cox proportional hazards model showed a significant trend between an increasing strain ratio (SR) (P < 0.001 for trend) and the total hemoglobin concentration (TTHC) (P = 0.001 for trend) and DFS. In the subgroup analysis, an intensified association between SR and DFS was observed among women who were progesterone receptor (PR)-positive, lower Ki-67 expression, HER2 negative, and without adjuvant chemotherapy and without Herceptin treatment (all P < 0.05 for interaction). Significant interactions between TTHC status and the lymphovascular invasion, estrogen receptor (ER) status, PR status, HER2 status, and Herceptin treatment were found for DFS(P < 0.05).The imaging–clinical combined model (TTHC + SR + clinicopathological variables) proved to be the best prediction model (AUC = 0.829, 95% CI = 0.786–0.872) and was identified as a potential risk stratification tool to discriminate the risk probability of recurrence.ConclusionThe combined imaging-clinical model we developed outperformed traditional clinical prognostic indicators, providing a non-invasive, reliable tool for preoperative DFS risk stratification and personalized therapeutic strategies in T1 BC. These findings underscore the importance of integrating advanced imaging techniques into clinical practice and offer support for future research to validate and expand on these predictive methodologies.

Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm

AbstractObjectiveThe objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)‐generated cancer maps versus MRI and conventional nomograms.Materials and methodsWe retrospectively analysed data from 147 patients who received MRI‐targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI‐based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI‐visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison.Postsurgical specimens were processed using whole‐mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test.ResultsUnfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient‐level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (−60%) and posterior prostate (−40%).ConclusionsUnfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve‐spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI‐based cancer mapping may lead to better oncological and functional outcomes for patients.

Optimized seismic hazard and structural vulnerability model considering macroseismic intensity measures

Macroseismic intensity measures are fundamental quantitative indicators for evaluating the vulnerability and resilience of regional building clusters. The vulnerability model of building portfolios established can provide a noteworthy reference for the development of large-scale regional earthquake risk distributions. However, there are differences in the quantitative scales of the macroseismic intensity standards used in multiple regions. The positive effects of instrument intensity and probabilistic seismic hazard theory on the vulnerability assessment of regional buildings are rarely considered, resulting in significant differences in their assessment results. This paper comprehensively considers the impact of instrument intensity and seismic hazard models on traditional macroseismic intensity. An updated macroseismic intensity measure (UCMS-20) is proposed for evaluating regional buildings, which is combined with the latest version of China's seismic intensity standards. The actual seismic damage sample dataset (31,649 buildings) of four types of building clusters in three typical earthquakes in China (Wenchuan 2008, Yushu 2010, and Jiuzhaigou 2017) has been expanded and updated. Using the UCMS-20, the European Macroseismic Scale (EMS-98), the Modified Mercalli intensity (MMI), and the Medvedev, Sponheuer, and Karnik Scale (MSK-64), vulnerability assessments are conducted on the extended empirical structural seismic damage dataset, and an empirical disaster matrix model considering updating damage states is established. A nonlinear vulnerability prediction model considering updated macrointensity measures is proposed, and the optimized model is validated and compared against the developed multiscale empirical structure dataset. The multiscale vulnerability index calculation model is improved, and novel optimization curves, point clouds, and matrix probability risk models are established, considering the expansion of empirical structural vulnerability databases.

An integrated urban flooding risk analysis framework leveraging machine learning models: A case study of Xi'an, China

As global climate change intensifies and urbanization progresses, the frequency and extent of urban flooding have been increasing. This trend poses a significant threat to the safety and property of urban residents and severely impedes the advancement of sustainable development goals (SDGs). This study proposed an integrated urban flooding risk analysis framework to provide an essential basis for developing effective adaptation and mitigation measures. The kernel density estimation was adopted to analyze the distribution characteristics of urban flooding hotspots. The maximum entropy (MaxEnt) model was adopted for urban flooding risk probability assessment based on multi-source data. This study conducted an in-depth analysis from three perspectives including spatial analysis, risk planning and model comparison. The MaxEnt model and multiple exploratory spatial data analysis methods were combined to reveal spatial distribution patterns at grid cell scale. The MaxEnt and Zonation models were coupled to generate an urban flooding risk map. Further, six traditional machine learning models were compared with the MaxEnt model. The results indicate that rapid urbanization and intense human activities have significantly increased the number of buildings, leading to loss of previously existing blue and green spaces. These urban characteristics increase the vulnerability of cities to extreme rainfall. Compared to traditional machine learning models, the MaxEnt model has significant advantages. In the field of urban flooding risk, the MaxEnt model demonstrates significant potential for practical application. In summary, the framework empowers urban managers to comprehensively evaluate flooding risk in planning, supporting urban design and risk prevention.

Towards the sustainable development of water security: A new copula-based risk assessment system

Water security is deeply intertwined with ecosystems, society and the economy, and is a frequent focal point of sustainable development. This study investigates the water security (WS) of Inner Mongolia, a dryland region in northern China. Specifically, an indicator system integrating the principles of Stability (S), Cooperation (C), and Resilience (R) was constructed to quantitatively and comprehensively assess water security. Risk probability was calculated by the constructed system by combining its output with a copula function. The findings revealed that: (1) WS-SCR levels in the study area gradually increased from 2001 to 2021. The periods multi-year average was 0.42, which is at the threshold level of security; (2) Based on a standard deviation ellipse, the spatial distribution of WS-SCR levels changed from northeast to southwest and back to northeast. The centroid of change shifted from east to west and back to east; (3) Key drivers influencing water security included GDP per capita (S3), agricultural irrigation water consumption per unit area (C4), comprehensive water use per capita (C5), reclaimed water use rate (C8), fertilizer application per unit of farmland (R4), and population density (R5); (4) The S-C, S-R, and C-R relationships within the study area were best modelled by the Clayton copula function, whereas the S-C-R relationship was most accurately modelled by using a Frank copula function. The three-dimensional joint risk probability S-C-R (S≤0.4, C≤0.4, R≤0.4) is 0.226; the probability that R was insecure increased with increasing S or C index values. This study provides a scientific foundation for the long-term planning and management of sustainable water security development in Eurasian drylands.

Identification of critical drought thresholds affecting vegetation on the Mongolian Plateau

The increasing frequency of drought events due to global climate change has significant impacts on ecosystems, often resulting in reduced vegetation productivity. However, vegetation responses to drought often exhibit non-linear threshold phenomena, where vegetation growth sharply decreases once water stress reaches a certain level. Our study, conducted on the Mongolian Plateau, focuses on revealing the drought response thresholds and distribution patterns of different vegetation types. These thresholds mark critical turning points from high drought resistance to high vulnerability in vegetation response and serve as early warning indicators, crucial for accurately assessing how terrestrial ecosystems adapt to climate change. We utilized remote sensing observation datasets and inversion datasets spanning from 2001 to 2020, including six representative vegetation indices. The results indicate that: (1) Over 70 % of the Mongolian Plateau experiences low vegetation growth (<10th) predominantly influenced by drought, with low vegetation growth often occurring when water availability falls below the 30th percentile; (2) Areas with higher vegetation coverage and relatively moist climates have lower proportions of drought thresholds, implying that more complex ecosystems can better mitigate water stress, while background climatic conditions also regulate vegetation response to water availability; (3) According to multi-model climate predictions, the overall probability of drought risk on the Mongolian Plateau is projected to significantly increase during 2080–2100, especially in drought-prone regions. These findings provide important insights into better understanding the impacts of drought on ecosystems. Additionally, they emphasize the urgency of taking early measures to adapt to future drought risks.

Experimental study on overtopping failure of concrete face rockfill dam

With the frequent occurrence of natural disasters, the problem of dam failure with low probability and high risk has gradually attracted people's attention. This paper uses flume model tests to systematically analyze the overtopping failure mechanisms of concrete face rockfill dam (CFRD) and identify its failure modes. The tests reveal that the longitudinal erosion of the CFRD breach progress through stages of soil erosion, panel failures, and water flow stabilization. Meanwhile, the cross-section breach process involves the evolution of breach size in rockfill materials, including traceable erosion, lateral broadening, and breach morphology stabilization. The fracture characteristics of the water-blocking panel are primarily evident in the flow-time curve. By analyzing the breach morphology evolution processes in longitudinal and cross sections, the flow-time curve can be subdivided into stages of burst flow formation, breach expansion with flow increase, rapid increase of breach flow discharge due to panel failures, and stabilization of breach flow and size. The primary damage process of the CFRD occurs in a cyclical stage of breach expansion, flow increase, panel failure, and rapid discharge. The rigid face plate and granular body structure contribute to partial dam failure, showing a tendency for gradual expansion of the breach. The longitudinal section illustrates dam failure resulting from panel fracture and rockfill erosion interaction, while downstream slopes exhibit failure due to lateral intrusion of rockfill and cyclic instability. These research results can serve as a reference for constructing a concrete CFRD failure prediction model and conducting disaster risk assessments.

The sensitivity of agricultural biogas plants to changes in energy prices in Poland

In Poland, despite ambitious plans from 15 years ago, when it was assumed that by 2020, an agricultural biogas plant would be standard in every commune, the potential of agricultural biogas has not been used due to the lack of stable legal regulations and financing programmes for the construction of the plants. The situation has now changed due to new forms of support systems, which motivated the authors to compare two agricultural biogas plants operating in the certificate system and the support system in the form of feed-in premiums. Moreover, the authors pointed to differences in the number of agricultural biogas plants and their capacity by voivodeship due to changes in legal conditions in force in two periods: from 1 January 2011, to 30 June 2016, and from 1 July 2016, to 19 January 2024. Based on the research methods used ‒ including Earnings Before Interest, Taxes Depreciation and Amortisation, the Internal Rate of Return, risk matrix and data analysis in spatial terms ‒ it was indicated that: 1. agricultural biogas plants are characterised by very high sensitivity related to the probability of risk in the case of investment costs, substrate prices and changes in energy prices; 2. financial support is important at the stage of construction of a biogas plant, which largely makes it possible to shorten the payback period and thus increase the willingness of future investors to invest in biogas plants and 3. in the periods analysed, significant differences were noted in the spatial location of biogas plants due to trends towards lower-power biogas plants, which is probably dictated by the constant and predictable premium system in the new support system.

Bioaccumulation dynamics, noncarcinogenic and carcinogenic risks of heavy metals in commercially valuable shellfish and finfish species from the world largest floating slum, Makoko, Nigeria

This study examined ten heavy metals in five species: Macrobrachium vollenhovenii, Penaeus monodon, P. notialis, Chloroscombrus chrysurus, and Pseudotolithus typus, from Makoko floating slum, Lagos Lagoon to discern their bioaccumulation potentials, sources of origin, and health implications. The concentrations were in this order: Fe (4.172–10.176) > Zn (1.310–5.754) > Mn (0.475–2.330) > Cu (0.238–1.735) > Pb (0.121–0.391) > Cd (0.055–0.283) > Co (0.056–0.144) > Ni (0.039–0.121) > Cr (0.022–0.095) > As (0.003–0.031) mg/kg. The MPDI denotes “low toxicity,” and the BAF/BSAF revealed that benthic species had higher bioconcentration potentials. Multivariate analyses revealed that heavy metals exhibited mutual relationships during chemical transport, and their sources were both geogenic and human-induced. The HI values were below 1, and the TCR values were below the threshold of 1 × 10−4. This suggests that the probabilities of noncancer and carcinogenic risks in human populations due to long-term consumption of the evaluated species are unlikely.

Development of preoperative nomograms to predict the risk of overall and multifocal positive surgical margin after radical prostatectomy

ObjectiveTo develop preoperative nomograms using risk factors based on clinicopathological and MRI for predicting the risk of positive surgical margin (PSM) after radical prostatectomy (RP).Patients and methodsThis study retrospectively enrolled patients who underwent prostate MRI before RP at our center between January 2015 and November 2022. Preoperative clinicopathological factors and MRI-based features were recorded for analysis. The presence of PSM (overall PSM [oPSM]) at pathology and the multifocality of PSM (mPSM) were evaluated. LASSO regression was employed for variable selection. For the final model construction, logistic regression was applied combined with the bootstrap method for internal verification. The risk probability of individual patients was visualized using a nomogram.ResultsIn all, 259 patients were included in this study, and 76 (29.3%) patients had PSM, including 40 patients with mPSM. Final multivariate logistic regression revealed that the independent risk factors for oPSM were tumor diameter, frank extraprostatic extension, and annual surgery volume (all p < 0.05), and the nomogram for oPSM reached an area under the curve (AUC) of 0.717 in development and 0.716 in internal verification. The independent risk factors for mPSM included the percentage of positive cores, tumor diameter, apex depth, and annual surgery volume (all p < 0.05), and the AUC of the nomogram for mPSM was 0.790 in both development and internal verification. The calibration curve analysis showed that these nomograms were well-calibrated for both oPSM and mPSM.ConclusionsThe proposed nomograms showed good performance and were feasible in predicting oPSM and mPSM, which might facilitate more individualized management of prostate cancer patients who are candidates for surgery.

Research on accident early warning of metallurgical enterprises based on grey DEMATEL/ISM and Bayesian network

To clarify the complex relationship between the factors causing safety accidents in metallurgical enterprises and predict the risk of accidents in enterprises, a correlation analysis model of the factors causing safety accidents in metallurgical enterprises based on grey Decision-Making Trial and Evaluation Laboratory/Interpretative Structural Modeling (DEMATEL/ISM) was established, and a Bayesian network early warning model was constructed on this basis. The relationship and action path of accident-causing factors in metallurgical enterprises were clarified. The factors were hierarchically divided and a multi-layer hierarchical structure model was established to obtain the neighboring cause, transitional cause, and essential cause of the accident. The results showed that the employee violation rate, the hazardous substances reserves, the toxic gas and dust pollution control compliance rate, the pass rate for equipment maintenance, and the qualification rate of special equipment were the neighboring causes of the accident. The perfection of the safety production management system was the essential cause. The Bayesian network early warning model was applied to the Fuxin Jiuxing Titanium work site. The expected risk probability of an accident was 17.9%, which was in a comparatively safe state (State2). The results obtained by the Bayesian model are consistent with those obtained by AHP and fuzzy comprehensive evaluation method, which proved the accuracy of the early warning model. The Bayesian model can give the risk probability value of the accident and the risk probability value of the accident cause factors at the same time, and include the causal relationship and conditional correlation relationship among the indicator variables in the reasoning process, which can provide targeted technical support for the construction of the emergency system of risk classification management and control.

Credit Card Client’s Payment Prediction for Next Month Using Machine Learning Algorithms

With the quick growth of the credit card system, there is a rising number of misconduct rates on credit card loans, which creates a financial risk for commercial banks. Thus, successful resolutions of the risks are significant for the sound advancement of the industry in the long term. Numerous financial banks and organizations become more and more attentive to the issue of credit card default because it brings about a high probability of financial risks. Credit risk plays a significant part in the financial business. One of the main functions of a bank is to issue loans, credit cards, investment mortgages, and other credit. One of the most popular financial services offered by banks in recent years has been the credit card. With its constant rise in risk factors, the banking industry is perhaps the most fragile and volatile in the world. Credit risk remains a crucial element for financial institutions that have experienced losses amounting to hundreds of millions of dollars as a result of their incapacity to retrieve the funds disbursed to clients. In the banking industry, it is now vital to forecast whether a borrower will be able to repay the loan. In this paper, we applied different machine learning classifiers, including Random Forest, K Nearest Neighbor, Logistic Regression, Decision Tree, Decision Tree with AdaBoosting, and Random Forest with AdaBoosting, to build a credit default prediction model. The results show that the AdaBoosting model achieved better accuracy than the other machine learning algorithms. Our proposed technique can support financial organizations in controlling, identifying, and monitoring credit risk, and it can identify credit card clients who pay the loan in the next month.

Failure risk management: adaptive performance control and mission abort decisions.

The failure behavior of safety-critical systems typically depends on the system performance level, which offers opportunities to control system failure risk through dynamic performance adjustment. Moreover, mission abort serves as an intuitive way to mitigate safety hazards during mission execution. Our study focuses on systems that execute successive missions with random durations. To balance mission completion probability and system failure risk, we examine two decision problems: when to abort missions and how to select the performance level prior to mission abort. Our objective is to maximize the expected revenue through dynamic performance control and mission abort (PCMA) decisions. We consider condition-based PCMA decisions and formulate the joint optimization problem into a Markov decision process. We establish the monotonicity and concavity of the value function. Based on this insight, we show that optimizing the mission abort policy requires a series of control limits. In addition, we provide conditions under which the performance control policies are monotone. For comparative purposes, we analytically evaluate the performances of some heuristic policies. Finally, we present a case study involving unmanned aerial vehicles executing power line inspections. The results indicate the superiority of our proposed risk control policies in enhancing operational performance for safety-critical systems. Dynamic performance adjustment and mission abort decisions provide opportunities to reduce the failure risk and increase operational rewards of safety-criticalsystems.

High prevalence of malnutrition in an internal medicine department: results from the AMIDO study

The prevalence of malnutrition in patients is very high in Europe (&gt;70%). An Italian FADOI-SINPE survey revealed that malnutrition is an underestimated problem by internists. The AMIDO study examines malnutrition in Acqui Terme Hospital (Alessandria, Italy) Internal Medicine Department patients. Patients were screened for malnutrition using the Malnutrition Universal Screening Tool (MUST) upon admission. 268 patients (49.63% male) were enrolled, with a median age of 83 years. At admission, the prevalence of malnutrition risk was 48.88%. The proportion of patients who came from home resulted to have a slight decreasing trend among the three subgroups according to MUST (84.67%, 82.76%, and 73.53% in increasing order of MUST score respectively), but was not statistically significant (P=0.10). Dementia [odds ratio (OR): 6.36; 95% confidence interval (CI): 2.52-18.63], heart failure (OR: 2.45; 95% CI: 1.33-4.57), neoplasm (OR: 2.24; 95% CI: 1.08-4.77) and infectious diseases (OR: 2.27; 95% CI: 1.226-4.274) increase malnutrition odds. Increasing attention to malnutrition risk is crucial for patients with dementia, neoplasm, heart failure, and infectious diseases, which raise malnutrition risk probability.

Assessing the health risks of dermal exposure to heavy metals dust among nail salon technicians.

Nail salon technicians are susceptible to potential exposure to a diverse array of hazardous chemicals in the form of dust or vapors. One of the main routes of exposure is dermal contact. The aim of present study was to health risk assessment of dermal exposure to dust containing heavy metals in nail salon technicians. Dust sampling was done on the work surface of 20 available nail salon technicians. The concentration of five metals including cadmium, lead, chromium, nickel, and manganese were determined using ICP-MS. Afterwards, the United States Environmental Protection Agency (USEPA) guideline was used to estimate the potential health risks, including carcinogenic and non-carcinogenic risks, associated with the analyzed metals. Results indicated the mean concentrations of Pb, Cd, Ni, Cr and Mn were 0.7953±0.4373, 0.0952±0.0264, 0.7666±0.8629, 0.4900±0.5994 and 1.134±0.4736, respectively. The hazard quotient (HQ) of all metals was within the permissible value, while hazard index (HI) was greater than 1. The probability of cancer risk (CR) resulting from dermal exposure to Ni, Cd and Cr exceeded the acceptable risk levels (10-6-10-4), but CR calculated for Pb was less than allowable value. Implementation of engineering controls such as downdraft vented nail tables and portable source capture systems is necessary. Besides, the use of personal protective equipment such as disposable nitrile gloves, N95 respirator masks, and ensuring proper training on safe work practices is recommended.

Prevention and adaptation of intermodal interactive seaports and dry ports under asymmetric risk behavior

The port-hinterland system, encompassing seaports and dry ports, has evolved into the backbone of international trade. Due to their unique geographic locations and critical socioeconomic functions, ports are particularly susceptible to a range of risks. Port authorities (PAs) can address these challenges through targeted preventive efforts, which aims to reduce the probability of risk occurrence, and adaptive efforts, which seeks to mitigate potential damage. However, intermodal interactions and the asymmetric risk behaviors exhibited by ports under risk probability ambiguity present significant challenges for PAs making risk-responsive investments. This study develops a two-stage game model to examine the effects of asymmetric risk behavior and risk probability ambiguity on strategic investment decisions. Our analysis reveals that prevention are susceptible to free-rider effects, which are further exacerbated by risk behaviors and mitigated by investment strategy differentiation. Moreover, we demonstrate that, as a general guideline, seaport authorities should prioritize adaptation, while dry port authorities should focus on prevention, with adaptation being the primary strategy when the level of risk aversion is low. These findings provide valuable managerial insights for improving the risk management and resilience of the port-hinterland system.