Search Results (163)

Ammonia bunkering is becoming increasingly important in the maritime industry as ammonia is recognized as a viable alternative fuel for reducing carbon emissions in shipping. Bunkering by tank truck plays a crucial role in the early stages of ammonia-fueled ship development. It involves the efficient transportation of ammonia from production facilities to bunkering stations, offering flexibility in refueling vessels at ports, including those lacking extensive infrastructures like pipelines or large storage tanks. However, the safety and regulations surrounding ammonia use in bunkering are paramount to its adoption. This study focuses on analyzing the effectiveness of safeguards designed to reduce the frequency of ammonia releases and mitigate potential leak damage during bunkering operations. We examine how safeguards, such as breakaway couplings and dry disconnect couplings (DDC), can reduce leak occurrences, while excess flow valves (EFVs) and automatic emergency shut-off valves (ESVs) can limit the consequences of such incidents. If the breakaway coupling and DDC are implemented as safeguards in the flexible hose, and maintenance is performed in accordance with ANSI/CGA G-2.1, the probability of hose failure per bunkering operation will be reduced from approximately 10⁻⁵ to 10⁻⁷. Under the worst weather conditions during the day, the probit value (Pr) depends on both the amount of ammonia released and the distance from the release point, with the distance having a greater effect on fatality than the amount of ammonia. The individual risk is analyzed to determine whether the bunkering process using tank trucks is acceptable. The analysis concludes that, with these safeguards in place, the individual risk at a location 20 m from the bunker site can be reduced to the lower limit of the As Low As Reasonably Practicable (ALARP) zone, ensuring a safe and acceptable level of risk for ammonia bunkering operations. The safety integrity level (SIL) of the automatic ESV should be at least 2 or higher, and it should be activated within a few seconds after a gas leak begins. Full article

This study develops a computational framework for the simultaneous quantification of seismic resilience and economic losses in corrosion-affected coastal continuous girder bridges. The proposed model integrates adjustment factors to reflect delays in post-earthquake repairs and cost increments caused by progressive material degradation. Finite element methods and nonlinear dynamic time-history simulations were conducted on an existing coastal continuous girder bridge to validate the proposed model. The key innovation lies in a probability-weighted resilience index incorporating damage state occurrence probabilities, which overcomes the computational inefficiency of traditional recovery function approaches. Key findings demonstrate that chloride exposure duration exhibits a statistically significant positive association with earthquake-induced structural failure probabilities. Sensitivity analysis reveals two critical patterns: (1) a 0.3 g PGA increase causes a 11.4–18.2% reduction in the resilience index (RI), and (2) every ten-year extension of corrosion exposure decreases RI by 2.7–6.2%, confirming seismic intensity’s predominant role compared to material deterioration. The refined assessment approach reduces computational deviation to ±2.4%, relative to conventional recovery function methods. Economic analysis indicates that chloride-induced aging generates incremental indirect losses ranging from $58,000 to $108,000 per decade, illustrating compounding post-disaster socioeconomic consequences. This work systematically bridges corrosion-dependent structural vulnerabilities with long-term fiscal implications, providing decision-support tools for coastal continuous girder bridges’ maintenance planning. Full article

Bugs, especially those in concurrent systems, are often hard to reproduce because they manifest only under rare conditions. Testers frequently encounter failures that occur only under specific inputs, often at low probability. We propose an approach to systematically amplify the occurrence of such elusive bugs. We treat the system under test as a black-box system and use repeated trial executions to train a predictive model that estimates the probability of a given input configuration triggering a bug. We evaluate this approach on a dataset of 17 representative concurrency bugs spanning diverse categories. Several model-based search techniques are compared against a brute-force random sampling baseline. Our results show that an ensemble stacking classifier can significantly increase bug occurrence rates across nearly all scenarios, often achieving an order-of-magnitude improvement over random sampling. The contributions of this work include the following: (i) a novel formulation of bug amplification as a rare-event classification problem; (ii) an empirical evaluation of multiple techniques for amplifying bug occurrence, demonstrating the effectiveness of model-guided search; and (iii) a practical, non-invasive testing framework that helps practitioners to expose hidden concurrency faults without altering the internal system architecture. Full article

Background/Objectives: Visual inspection of parenteral drug products is a mandatory and critical unit operation, typically followed by an Acceptable Quality Level (AQL) check, as required by current Good Manufacturing Practices (cGMP) and regulatory authorities worldwide. Visual inspection and AQL checks need to ensure—probabilistically and statistically—that sterile product units with critical, major, or minor defects are excluded from the acceptable portion of a batch, thereby preventing such defective units from reaching distribution and eventually patients. Despite clearly defined batch defect categories, classifying individual defects and assigning them to the correct category remains challenging and has historically lacked standardization and scientific rationale. This paper presents a science-based risk assessment methodology for categorizing defects in sterile dosage forms, incorporating considerations of severity (with emphasis on patient safety), probability of occurrence, and probability of detection. Methods: The methodology is based on a modified Failure Mode and Effects Analysis (FMEA), tailored specifically for visual inspection defect classification. Results: Three examples demonstrate the practical application of this risk-based approach across different container formats: vials, pre-filled syringes, and cartridges. Conclusions: This standardized methodology offers a clear, consistent, and scientifically justified framework for defect classification. Its use enables pharmaceutical manufacturers to establish robust, risk-based defect categorization for the visual inspection of clinical and commercial sterile products. Full article

In recent years, extreme weather events have occurred more frequently. The resulting equipment failure, renewable energy extreme output, and other extreme operation scenarios affect the smooth operation of power grids. The occurrence probability of extreme operation scenarios is small, and the occurrence frequency in historical operation data is low, which affects the modeling accuracy for scenario generation. Meanwhile, extreme operation scenarios in the form of discrete temporal data lack corresponding modeling methods. Therefore, this paper proposes a definition and generation framework for extreme power grid operation scenarios triggered by extreme weather events. Extreme operation scenario expansion is realized based on the sequential Monte Carlo sampling method and the distribution shifting algorithm. To generate equipment failure scenarios in discrete temporal data form and extreme output scenarios in continuous temporal data form for renewable energy, a Gumbel-Softmax variational autoencoder and an extreme conditional generative adversarial network are respectively proposed. Numerical examples show that the proposed models can effectively overcome limitations related to insufficient historical extreme data and discrete extreme scenario training. Additionally, they can generate improved-quality equipment failure scenarios and renewable energy extreme output scenarios and provide scenario support for power grid planning and operation. Full article

Peripheral artery disease (PAD) is associated with an elevated risk of major adverse cardiovascular events (MACE). Despite this, few reliable biomarkers exist to identify patients at heightened risk of MACE. Growth differentiation factor 15 (GDF15), a stress-responsive cytokine implicated in inflammation, atherosclerosis, and thrombosis, has been broadly studied in cardiovascular disease but remains underexplored in PAD. This study aimed to evaluate the prognostic utility of GDF15 for predicting 2-year MACE in PAD patients using explainable statistical and machine learning approaches. We conducted a prospective analysis of 1192 individuals (454 with PAD and 738 without PAD). At study entry, patient plasma GDF15 concentrations were measured using a validated multiplex immunoassay. The cohort was followed for two years to monitor the occurrence of MACE, defined as stroke, myocardial infarction, or death. Baseline GDF15 levels were compared between PAD and non-PAD participants using the Mann–Whitney U test. A machine learning model based on extreme gradient boosting (XGBoost) was trained to predict 2-year MACE using 10-fold cross-validation, incorporating GDF15 and clinical variables including age, sex, comorbidities (hypertension, diabetes, dyslipidemia, congestive heart failure, coronary artery disease, and previous stroke or transient ischemic attack), smoking history, and cardioprotective medication use. The model’s primary evaluation metric was the F1 score, a validated measurement of the harmonic mean of the precision and recall values of the prediction model. Secondary model performance metrics included precision, recall, positive likelihood ratio (LR+), and negative likelihood ratio (LR-). A prediction probability histogram and Shapley additive explanations (SHAP) analysis were used to assess model discrimination and interpretability. The mean participant age was 70 ± SD 11 years, with 32% (n = 386) female representation. Median plasma GDF15 levels were significantly higher in PAD patients compared to the levels in non-PAD patients (1.29 [IQR 0.77–2.22] vs. 0.99 [IQR 0.61–1.63] pg/mL; p < 0.001). During the 2-year follow-up period, 219 individuals (18.4%) experienced MACE. The XGBoost model demonstrated strong predictive performance for 2-year MACE (F1 score = 0.83; precision = 82.0%; recall = 83.7%; LR+ = 1.88; LR− = 0.83). The prediction histogram revealed distinct stratification between those who did vs. did not experience 2-year MACE. SHAP analysis identified GDF15 as the most influential predictive feature, surpassing traditional clinical predictors such as age, cardiovascular history, and smoking status. This study highlights GDF15 as a strong prognostic biomarker for 2-year MACE in patients with PAD. When combined with clinical variables in an interpretable machine learning model, GDF15 supports the early identification of patients at high risk for systemic cardiovascular events, facilitating personalized treatment strategies including multidisciplinary specialist referrals and aggressive cardiovascular risk reduction therapy. This biomarker-guided approach offers a promising pathway for improving cardiovascular outcomes in the PAD population through precision risk stratification. Full article

The reliability of the electro-power equipment of electrical power transmission systems is essential in ensuring an uninterrupted power supply with the necessary voltage and frequency stability. This is especially important when performing lengthy procedures requiring the serviceability of the electrical equipment used, such as those related to foundries and metallurgical processes, or with the processes of testing complex means for the remote control of electromagnetic radiation within the implementation of the Sustainable development of the Competence Center “Quantum Communication, Intelligent Security Systems and Risk Management” (QUASAR) Project, funded with the participation of the EU under the “Research, Innovation and Digitalization for Smart Transformation” Program 2021.2027 according to procedure BG16RFPR002-1.014. One of the main issues in this case is related to the availability of information regarding the technical condition of the deployed or reserve energy resources. In this connection, this study proposes methods for determining the quantity of operational equipment that is either in use or in storage, based on the reliability testing of a representative sample of it. Full article

Polyurea failures in reinforced concrete tanks, such as swimming pools and sewage treatment plants, require a thorough analysis of the causes of failures during renovation. Urban agglomerations are increasingly relying on these facilities for maintaining city functioning, and the increasing concentration of pollutants in these facilities necessitates urgent repairs due to frequent failures. More thorough analysis should be given to repeated failures on the same object or “twin” objects within a short period, causing high renovation costs and long shutdowns. The causes of failures can be found not only as a result of insufficient knowledge but also in a limited analysis of the entire project from the assumption phase to completion. The article analyzed water and sewage tanks on which failures of applied polyurea coatings occurred many times. The posteriori uses of the risk management analysis with the assessment of the impact and probability of occurrence of the planned activities that failed allows it to be applied a priori and treated as a necessary analysis. For this purpose, in selected repairs, those activities that had the greatest impact on failure and a relatively high probability of occurrence during implementation were distinguished from the entire project. Based on the risk management analysis, it was shown that the basic cause of the failure was the poor knowledge and insufficient experience of the entities performing the repairs, and the errors that occurred could be minimized by conducting good diagnostics of the facility, selecting professional designers and contractors, and constant monitoring of each important activity. Full article

Multimodal freight transport systems, integrating maritime, rail, and road modes, play a vital role in modern logistics but face elevated operational, human, and environmental risks due to their complexity and interdependencies. To address the limitations of conventional risk assessment methods, this study proposes a hybrid risk modeling framework that integrates fault tree analysis (FTA), dynamic fault trees (DFTs), and fuzzy logic reasoning. This approach supports the modeling of sequential failures and captures qualitative uncertainties such as human fatigue and inadequate training. The framework incorporates reliability metrics, including Mean Time to Failure (MTTF) and Mean Time Between Failures (MTBF), enabling the quantification of system resilience and identification of critical failure pathways. Application of the model revealed human error, particularly procedural violations, insufficient training, and fatigue, as the dominant risk factor across transport modes. Road transport exhibited the highest probability of risk occurrence (p = 0.9960), followed by rail (p = 0.9937) and maritime (p = 0.9900). By integrating probabilistic reasoning with qualitative insights, the proposed model offers a flexible decision support tool for logistics operators and policymakers, enabling scenario-based risk planning and enhancing system robustness under uncertainty. Full article

Under extreme operating conditions such as high temperatures, strong corrosion, and cyclic thermal shocks, key equipment in solar tower power plants (STPPs) is prone to severe accidents and results in significant losses. To systematically quantify potential failure risks and address the methodological gaps in existing research, this study proposes a risk assessment framework combining a novel scenario propagation model covering triggering factors, precursor events, accident scenarios, and response measures with an interval type-2 fuzzy set (IT2FS) Bayesian network. This framework establishes equipment failure evolution pathways and consequence evaluation criteria. To address data scarcity, the methodology integrates actual case data and expert elicitation to obtain assessment parameters. Specifically, an IT2FS-based similarity aggregation method quantifies expert opinions for prior probability estimation. Additionally, to reduce computational complexity and enhance reliability in conditional probability acquisition, the IT2FS-integrated best–worst method evaluates the relative importance of parent nodes, combined with a leakage-weighted summation algorithm to generate conditional probability tables. The model was applied to a western Chinese STPP and the results show the probabilities of receiver blockage, pipeline blockage, tank leakage, and heat exchanger blockage are 0.061, 0.059, 0.04, and 0.08, respectively. Under normal operating conditions, the occurrence rates of level II accident consequences for all four equipment types remain below 6%, with response measures demonstrating significant suppression effects on accidents. The research results provide critical decision-making support for risk management and mitigation strategies in STPPs. Full article

Landslide risk assessment can quantify the potential damage caused by landslides to disaster-bearing bodies, which can help to reduce casualties and economic losses. It is not only a tool for disaster prevention and mitigation, but also a key step to achieve the coordinated development of the environment, economy, and society, and it provides important support for the realization of the global sustainable development goals (SDGs). In this study, a risk assessment method is proposed for an individual landslide based on the non-probabilistic reliability theory. The method represents an improvement to and innovation in existing risk assessment methods, which can obtain more accurate assessment results with fewer sample data points, refines the methods and steps of landslide risk assessment, and fully considers the destabilization mechanism of the landslide and the interaction with disaster-bearing bodies. A non-probabilistic reliability analysis of the slope was conducted, and the possibility of landslide occurrence was characterized by the failure value of the slope. Moreover, the influence range of the landslide was predicted using empirical formulas; space–time distribution probabilities of the disaster-bearing bodies were estimated by combining their location and activity patterns; and the vulnerability of the disaster-bearing bodies was calculated according to the landslide intensity and the resistance or susceptibility index of the disaster-bearing bodies. The method’s feasibility was verified through its application to the Xiatudiling landslide as a case study. In the process of performing slope stability calculations, it was found that the calculation results of the Monte Carlo method were consistent with those of the non-probabilistic reliability approach proposed in this paper, which was able to obtain more accurate results with less sample data. The personnel life and economic risks were 1.8499 persons/year and CNY 184,858/year (USD 25,448/year), respectively, under heavy rainfall conditions. The results were compared with the risk judgment criteria for geological disasters, and both risk values were unacceptable. After landslide treatment, the possibility of landslide occurrence was reduced, and the personnel life risk and economic risk of the landslide were also reduced. Both risk values then became acceptable. The effect of landslide treatment was obvious. The proposed method provides a new technique for assessing landslide risks and can help in designing mitigation strategies. This method can be applied to landslide risk surveys conducted by geological disaster prevention institutions, demonstrating enhanced applicability in data-scarce regions to improve risk assessment efficiency. It is particularly suitable for emergency management authorities, enabling rapid and comprehensive assessment of landslide risk levels to support informed decision making during critical response scenarios. Full article

A single batch of AlSi10Mg powder was used to fabricate two groups of round bars via horizontal printing, employing an identical strategy except for one parameter in the process of powder bed fusion-laser beam. The parameter is layer thickness, set at 50 and 80 μm for Group-1 and Group-2, respectively, resulting in laser energy densities of 29.95 and 18.72 J/mm³. Both materials exhibit similar microstructures; Group-1 has fewer and smaller defects than Group-2, leading to higher strength and ductility. Fatigue performance of low-cycle and long-life up to 10⁸ cycles under rotating bending was assessed, and the fracture surfaces were carefully examined under scanning electron microscopy. The S-N data converge to a single slope followed by a horizontal asymptote, indicating the occurrence of very-high-cycle fatigue (VHCF) in both cases. Group-1 shows higher fatigue strength in the range of 10⁴ to 10⁸ cycles, and a greater failure probability in VHCF regime than Group-2. This is attributed to the larger defect size in Group-2, where the smaller control volume in rotating bending greatly increases the likelihood of encountering large defects compared to Group-1. At the defect edge, the microstructure shows distinct resistance to crack propagation under high and low loads. Full article

Gas distribution stations (GDSs), pivotal nodes in long-distance natural gas transportation networks, are susceptible to catastrophic fire and explosion accidents stemming from system failures, thereby emphasizing the urgency for robust safety measures. While previous studies have mainly focused on gas transmission pipelines, GDSs have received less attention, and existing risk assessment methodologies for GDSs may have limitations in providing accurate and reliable accident probability predictions and fault diagnoses, especially under data uncertainty. This paper introduces a novel dynamic accident probability assessment framework tailored for GDS under data uncertainty. By integrating Bayesian network (BN) modeling with fuzzy expert judgments, frequentist estimation, and Bayesian updating, the framework offers a comprehensive approach. It encompasses accident modeling, root event (RE) probability estimation, undesired event (UE) predictive analysis, probability adaptation, and accident diagnosis analysis. A case study demonstrates the framework’s reliability and effectiveness, revealing that the occurrence probability of major hazards like vapor cloud explosions and long-duration jet fires diminishes significantly with effective safety barriers. Crucially, the framework acknowledges the dynamic nature of risk by incorporating observed failure incidents or near-misses into the assessment, promptly adjusting risk indicators like UE probabilities and RE criticality. This underscores the importance for decision-makers to maintain a heightened awareness of these dynamics, enabling swift adjustments to maintenance strategies and resource allocation prioritization. By mitigating assessment uncertainty and enhancing precision in maintenance strategies, the framework represents a significant advancement in GDS safety management, ultimately striving to elevate safety and reliability standards, mitigate natural gas distribution risks, and safeguard public safety and the environment. Full article

The process of fully mechanized coal seam mining under aquifers and surface water bodies has been a challenge in recent years for different countries. During the evolution of subsidence and the overburdening of rock mass movement above the longwall goaf, there is always a potential risk of connecting the water-conducting fracture zone with aquifers. The water inflows in the coal mine’s roadways have a negative impact on the productivity of the working faces and pose significant hazards to miners in the event of water inrush. Therefore, the assessment of the height of the water-flowing fractured zone has an important scientific and practical significance. The background of this study is the Xinhu Coal Mine in Anhui Province, China. In the number 81 mining area of the Xinhu Coal Mine during the mining of the number 815 longwall, a water inflow occurred due to fractures in the sandstone in the overburden rock. The experience of the successful implementation of the water inrush control method by horizontal regional grouting through multiple directional wells is described in this paper. This study proposes an algorithm for the assessment of the risk of water inrush from aquifers, based on an ANSYS 17.2 simulation in the complex anticline coal seam bedding. It was found that the safety factors based on the stress and strain parameters can be used as criteria for the risk of rock failure in the aquifer zone. For the number 817 longwall panel of the Xinhu Coal Mine, the probability of rock mass failure indicates a low risk of the occurrence of a water-flowing fractured zone. Full article

Background and Clinical Significance: Statins are a widely used drug class associated with a plethora of muscular side effects ranging from the subclinical elevation of creatine kinase to fulminant rhabdomyolysis. Cardiac myopathy secondary to statin treatment is rare and was recently reported as a part of statin-induced necrotizing autoimmune myopathy (SINAM). Its occurrence outside of this context is still debated. Case Presentation: We present the case of a 60-year-old male who developed atorvastatin-induced rhabdomyolysis, without associated hydroxymethyl glutaryl coenzyme A reductase (HMGCR) antibodies, with clinical findings of cardiac failure and severe ECG anomalies. The symptoms slowly regressed with statin withdrawal, and the patient made a full recovery. We discuss the recently proposed statin-associated cardiomyopathy (SACM) and the possible mechanisms. We compare our case to the three other cases of statin-induced cardiac myositis found in the literature. Conclusions: We believe that in vulnerable patients, as was our case, statins can determine significant subacute cardiac toxicity. This would seem to occur in the context of severe skeletal muscle injury, probably due to higher metabolic resistance on the part of the myocardium. Also, the available evidence suggests myocardial involvement should be actively investigated in SINAM patients, preferably by cardiac MRI. Full article