Search Results (34)

Polycyclic aromatic hydrocarbons (PAHs) are major carcinogenic pollutants in urban air, and inhalation exposure poses health risks, particularly for primary school children aged 6–14 years in school environments. Traditional deterministic models for incremental lifetime cancer risk (ILCR) assessment often fail to adequately quantify variability and epistemic uncertainty in exposure parameters. This study develops a multi-layered probabilistic framework that progresses from deterministic calculations through one-dimensional Monte Carlo and sensitivity-guided two-dimensional Monte Carlo to a hierarchical (second-order) two-dimensional Monte Carlo simulation. The hierarchical approach samples hyper-parameters of the input distributions (means, standard deviations, and modes) in the outer loop, while exposure variables are sampled in the inner loop using Latin hypercube sampling. Applied to PAH and BaP_eq concentrations measured indoors and outdoors during heating and non-heating seasons, the framework yielded mean total ILCR values of 1.42 × 10⁻⁶ for children and 1.18 × 10⁻⁶ for adults. The hierarchical 2D MC produced 95% confidence intervals on the 95th percentiles of [9.17 × 10⁻⁷, 5.67 × 10⁻⁶] for children and [6.48 × 10⁻⁷, 5.57 × 10⁻⁶] for adults, with outdoor heating identified as the dominant exposure pathway. Although the air sampling campaign was conducted in 2011–2012, the data remain representative for evaluating seasonal and microenvironmental variability of PAHs in urban school settings in the region, as PAH levels are predominantly driven by persistent combustion sources. This framework provides more comprehensive uncertainty quantification for complex environmental exposure scenarios. Full article

Per- and Polyfluoroalkyl substances (PFASs) are commonly detected in airport stormwater runoff due to historical and ongoing use of aqueous film-forming foams (AFFFs). Conventional stormwater control measures (SCMs) are generally effective at removing PFASs associated with the particulate fraction, but may provide limited removal of dissolved-phase PFASs. Sorbent polishing beds represent a potential downstream treatment option; however, their applicability and performance for PFASs in stormwater have not been well studied. In this study, measured PFAS concentrations and runoff volumes from an AFFF-affected airport apron were combined with literature-derived sorption parameters to develop a screening-level framework for evaluating adsorber beds as polishing units for SCM effluent. Bed sizing was calculated using a representative empty bed contact time (EBCT) of 10 min and a design volume based on the 85th percentile storm event. Sorbent performance was evaluated using literature equilibrium partition coefficients (K_d) for activated carbons, ion exchange resins, and specialty materials to estimate operational lifetimes prior to regeneration or replacement. Model-based results indicated lifetimes ranging from approximately 7 years for activated carbon to more than 50 years for specialty materials, depending on PFAS chain length and affinity. Sensitivity analysis using quartile K_d ranges showed predicted lifetimes spanning orders of magnitude, emphasizing the screening-level nature of the estimates. This work links field monitoring data with conceptual adsorber design to support early-stage evaluation of sorbent polishing strategies for airport runoff management, supporting compliance under tightening discharge regulations. Full article

Despite global restrictions like the Minamata Convention, heavy metal contamination in cosmetics remains a critical public health concern, with limited cross-country comparative data on heavy metal concentrations in cosmetics across Asian markets. We measured Hg, Pb, As, Cd, Sb, Cr, and Ni contents in 189 cosmetic products purchased in 2022 in Bangladesh, India, Indonesia, Korea, Malaysia, the Philippines, and Vietnam. Samples were screened by handheld X-ray fluorescence; Hg was quantified by a direct mercury analyzer and As, Cd, Cr, Ni, Pb, and Sb were quantified by ICP-OES. Principal component analysis (PCA) was used to characterize metal co-occurrence patterns, and Monte Carlo simulation was applied to estimate dermal systemic exposure dose, hazard quotients (HQ), and lifetime cancer risk (LCR). Mercury in face creams exhibited extreme heterogeneity (range: ND-67,000 mg/kg), while eye cosmetics contained elevated Arsenic levels (median 4.13 mg/kg). PCA distinctively separated Hg (PC2) from geogenic metals (As/Cr/Ni on PC1), suggesting intentional adulteration. Probabilistic risk estimates indicated upper-tail non-cancer risk for Hg in facial creams (95th percentile HQ 6.32; P[HQ>1] = 24.4%). As produced the highest LCR estimates (facial cream 95th percentile 2.60 × 10⁻⁴). These findings indicate product-type-specific metal patterns and highlight a subset of facial products with extreme Hg levels that can drive substantial upper-percentile risk, supporting the need for targeted market surveillance and enforcement. Full article

Temperature and related thermal comfort metrics at a representative 9-member ensemble of airports in Europe are presented using a combination of historical (1985–2014) and future projection (2035–2064) timescales under a variety of forcing scenarios. Data are shown for summer (June–July–August) and the nine sites are further grouped into ‘oceanic’, ‘continentally influenced’, and ‘Mediterranean coastal’ climate types, which ameliorates visualisation and provides more generalised policy-relevant results. Using the Humidex metric, it is shown that some airports in southern Europe may enter a ‘dangerous’ (>45 °C) regime of human discomfort. This would be accompanied by economic impacts related to longer mandated rest periods for ground workers, as well as increased water intake and changes to health and safety training. The coincidence of the 38 °C flash point of kerosene jet fuel with perturbed daily maximum temperature occurrence thresholds at some sites will likely also have knock-on effects on safety practices since some sites may experience 70% of future summer days with temperatures exceeding this value. Using an 18 °C threshold for defining cooling and heating ‘degree days’, increases in cooling requirements are projected to be larger than reductions in heating for continental and Mediterranean sites, and heatwave occurrence (3 or more days at or above the 95th historical percentile) may increase by a factor of 10. From a building and infrastructure services perspective, increased temperature variability around larger average values has the potential to reduce safe runway lifetimes and increase structural fatigue in large-span steel terminal buildings. Full article

Electrical insulation systems (EISs) are the principal reliability bottleneck of modern electrical machines (EMs). Among the many stresses acting on insulation, thermal stress is the most pervasive because it accelerates chemical reactions that progressively erode dielectric and mechanical integrity, ultimately dictating service life. As EMs migrate into compact, high-power-density platforms—automotive, aerospace, and industrial drives—designers need lifetime models that are not merely explanatory but actionable, linking operating temperatures and missions to quantified ageing and risk. This review article traces the evolution of thermal-ageing modelling from fundamental chemistry to a practical design tool. The historical empirical lineage of Arrhenius equation, Arrhenius–Dakin model, and Montsinger model is first revisited, clarifying their assumptions, parameter definitions, and the construction of thermal endurance curves. A discussion then follows on extensions that address deviations from first-order kinetics and demonstrate how variable temperature histories can be incorporated through cumulative damage formulations suitable for duty-cycle analysis. Since models are required to be anchored in data, accelerated thermal ageing (ATA) practices on representative specimens are outlined, alongside a description of the Weibull post-processing for deriving percentile lifetimes aligned with design targets. Building upon these foundations, the Physics-of-Failure (PoF) approach is introduced as a reliability-oriented design (ROD) methodology, in which validated lifetime models guide material selection and geometry optimisation while supporting prognostics and health management during operation. The emerging trend towards a hybrid PoF–AI approach is also discussed, which integrates artificial intelligence to identify nonlinear degradation patterns and drifting parameter relationships beyond the reach of empirical models, with physical constraints ensuring that predictions remain consistent with known ageing mechanisms. Such integration enables the learning process to adapt to operational variability and coupled stress effects, thereby improving both the accuracy and physical interpretability of lifetime estimation. The review aims to provide a concise view of models, tests, and workflows that convert thermal-ageing knowledge into robust, design-time decisions. By linking empirical and physics-based insights with modern data-driven learning, these developments support proactive maintenance, sustainable asset management, and extended operational lifetimes for next-generation EMs. Full article

Background: Polygenic risk scores (PRSs) aggregate the effects of many common genetic variants and are being investigated as tools to refine coronary artery disease (CAD) risk prediction beyond traditional clinical models. Methods and Results: We review the development of PRS from early unweighted scores to contemporary genome-wide models and summarize evidence from major studies. We identified key studies through PubMed searches using the terms “polygenic risk score,” “genetic risk prediction,” and “coronary artery disease,” supplemented by citation chaining of highly cited articles and recent reviews. Large cohorts, such as the UK Biobank, show that individuals in the highest PRS percentiles have a 3–5-fold higher risk of CAD, and may gain the greatest benefit from statin therapy. PRS can also reclassify younger adults at borderline or intermediate risk and may complement coronary artery calcium (CAC) scoring. Conclusions: PRSs hold promise for lifetime risk stratification and targeted prevention in CAD but are limited by ancestry bias in GWAS, underrepresentation of diverse populations, inconsistency in individual estimates, and lack of standardized reporting. Future research should focus on expanding multi-ancestry databases, standardizing methods, prospective validation, and effective communication strategies to support equitable and evidence-based clinical use. Full article

A significant challenge in the accelerated life test (ALT) is the reliance on large sample sizes and multiple stress levels, which results in high costs and long test durations. To address this issue, this paper develops a new reliability assessment method for small-sample ALTs with normal distribution (or lognormal distribution) and censoring. This method enables a high-confidence evaluation of the percentile lifetime (reliable lifetime) under normal operating stress level using censored data from only two accelerated stress levels. Firstly, a relationship is established between the percentile lifetime at normal stress level and the distribution parameters at accelerated stress levels. Subsequently, an initial estimate of the percentile lifetime is obtained from failure data, and its confidence is then refined using a Bayesian update with the nonfailures. Finally, an exact one-sided lower confidence limit (LCL) for the percentile lifetime and reliability is determined. This paper derives an analytical formula for LCLs under Type-II censoring scenarios and further extend the method to accommodate Type-I censored and general incomplete data. The Monte Carlo simulations and case studies show that, the proposed methods significantly reduce the required sample size and testing duration while offering superior theoretical rigor and accuracy than the conventional methods. Full article

This paper introduces a novel and flexible class of continuous probability distributions, termed the Alpha Power Survival Ratio-X (APSR-X) family. Unlike many existing transformation-based families, the APSR-X class integrates an alpha power transformation with a survival ratio structure, offering a new mechanism for enhancing shape flexibility while maintaining mathematical tractability. This construction enables fine control over both the tail behavior and the symmetry properties, distinguishing it from traditional alpha power or survival-based extensions. We focus on a key member of this family, the two-parameter Alpha Power Survival Ratio Exponential (APSR-Exp) distribution, deriving essential mathematical properties including moments, quantile functions and hazard rate structures. We estimate the model parameters using eight frequentist methods: the maximum likelihood (MLE), maximum product of spacings (MPSE), least squares (LSE), weighted least squares (WLSE), Anderson–Darling (ADE), right-tailed Anderson–Darling (RADE), Cramér–von Mises (CVME) and percentile (PCE) estimation. Through comprehensive Monte Carlo simulations, we evaluate the estimator performance using bias, mean squared error and mean relative error metrics. The proposed APSR-X framework uniquely enables preservation or controlled modification of the symmetry in probability density and hazard rate functions via its shape parameter. This capability is particularly valuable in reliability and survival analyses, where symmetric patterns represent balanced risk profiles while asymmetric shapes capture skewed failure behaviors. We demonstrate the practical utility of the APSR-Exp model through three real-world applications: economic (tax revenue durations), engineering (mechanical repair times) and medical (infection durations) datasets. In all cases, the proposed model achieves a superior fit over that of the conventional alternatives, supported by goodness-of-fit statistics and visual diagnostics. These findings establish the APSR-X family as a unique, symmetry-aware modeling framework for complex lifetime data. Full article

NAND flash memory has been widely adopted as the primary data storage medium in data centers. However, the inherent characteristic of out-of-place updates in NAND flash necessitates garbage collection (GC) operations on NAND flash-based solid-state drives (SSDs), aimed at reclaiming flash blocks occupied by invalid data. GC processes entail additional read and write operations, which can lead to the blocking of user requests, thereby increasing the tail latency. Moreover, frequent execution of GC operations is prone to induce more pages to be written, further reducing the lifetime of SSDs. In light of these challenges, we introduce an innovative GC scheme, termed SplitGC. This scheme leverages the records of data redundancy gathered during periodic read scrub operations within the SSD. By analyzing these features of data duplication, SplitGC enhances the selection strategy for the victim block. Furthermore, it bifurcates the migration of valid data pages into two phases: non-duplicate pages follow standard relocation procedures, whereas the movement of duplicate pages is scheduled during idle periods of the SSD. The experiment results show that our scheme reduces tail latency induced by GC by 8% to 83% at the 99.99th percentile and significantly decreases the amount of valid page migration by 38% to 67% compared with existing schemes. Full article

This study proposes a generalized family of distributions to enhance flexibility in modeling complex engineering and biomedical data. The framework unifies existing models and improves reliability analysis in both engineering and biomedical applications by capturing diverse system behaviors. We introduce a novel hybrid family of distributions that incorporates a flexible set of hybrid functions, enabling the extension of various existing distributions. Specifically, we present a three-parameter special member called the hybrid-Weibull–exponential (HWE) distribution. We derive several fundamental mathematical properties of this new family, including moments, random data generation processes, mean residual life (MRL) and its relationship with the failure rate function, and its related asymptotic behavior. Furthermore, we compute advanced information measures, such as extropy and cumulative residual entropy, and derive order statistics along with their asymptotic behaviors. Model identifiability is demonstrated numerically using the Kullback–Leibler divergence. Additionally, we perform a stress–strength (SS) reliability analysis of the HWE under two common scale parameters, supported by illustrative numerical evaluations. For parameter estimation, we adopt the maximum likelihood estimation (MLE) method in both density estimation and SS-parameter studies. The simulation results indicated that the MLE demonstrates consistency in both density and SS-parameter estimations, with the mean squared error of the MLEs decreasing as the sample size increases. Moreover, the average length of the confidence interval for the percentile and Student’s t-bootstrap for the SS-parameter becomes smaller with larger sample sizes, and the coverage probability progressively aligns with the nominal confidence level of 95%. To demonstrate the practical effectiveness of the hybrid family, we provide three real-world data applications in which the HWE distribution outperforms many existing Weibull-based models, as measured by AIC, BIC, CAIC, KS, Anderson–Darling, and Cramer–von Mises criteria. Furthermore, the HLW exhibits strong performance in SS-parameter analysis. Consequently, this hybrid family holds immense potential for modeling lifetime data and advancing reliability and survival analysis. Full article

The Hjorth distribution is more flexible in modeling various hazard rate shapes, including increasing, decreasing, and bathtub shapes. This makes it highly useful in reliability analysis and survival studies, where different failure rate behaviors must be captured effectively. In some practical experiments, the observed data may appear to be continuous, but their intrinsic discreteness requires the development of specialized techniques for constructing discrete counterparts to continuous distributions. This study extends this methodology by discretizing the Hjorth distribution using the survival function approach. The proposed discrete Hjorth distribution preserves the essential statistical characteristics of its continuous counterpart, such as percentiles and quantiles, making it a valuable tool for modeling lifetime data. The complexity of the transformation requires numerical techniques to ensure accurate estimations and analysis. A key feature of this study is the incorporation of Type-II censored samples. We also derive key statistical properties, including the quantile function and order statistics, and then employ maximum likelihood and Bayesian inference methods. A comparative analysis of these estimation techniques is conducted through simulation studies. Furthermore, the proposed model is validated using two real-world datasets, including electronic device failure times and ball-bearing failure analysis, by applying goodness-of-fit tests against alternative discrete models. The findings emphasize the versatility and applicability of the discrete Hjorth distribution in reliability studies, engineering, and survival analysis, offering a robust framework for modeling discrete data in practical scenarios. To our knowledge, no prior research has explored the use of censored data in analyzing discrete Hjorth-distributed data. This study fills this gap, providing new insights into discrete reliability modeling and broadening the application of the Hjorth distribution in real-world scenarios. Full article

Mobile source air toxics (MSATs) are major contributors to urban air pollution, especially near high-traffic roadways, where populations face elevated pollutant exposures. Traditional human health risk assessments, based on deterministic methods, often overlook variability in exposure and the vulnerabilities of sensitive subpopulations. This study introduces and applies a new stochastic modeling approach, utilizing Monte Carlo simulations to evaluate cumulative cancer risks from MSATs exposure through inhalation and ingestion pathways. This method captures variability in exposure scenarios, providing detailed health risk assessments, particularly for vulnerable groups such as children and the elderly. This approach was demonstrated in a case study conducted in Saint Paul, Minnesota, using 2019 traffic data. Deterministic models estimated cumulative cancer risks for adults at 6.24E-02 (unitless lifetime cancer risk), while stochastic modeling revealed a broader range, with the 95th percentile reaching 4.98E-02. The 95th percentile, used in regulatory evaluations, identifies high-risk scenarios overlooked by deterministic methods. This research advances the understanding of MSATs exposure risks by integrating spatiotemporal dynamics, identifying high-risk zones and vulnerable subpopulations, and supporting resource allocation for targeted pollution control measures. Future applications of this methodology include expanding stochastic modeling to evaluate ecological risks from mobile emissions. Full article

(1) Background: Obesity and overweight are defined as an abnormal or excessive accumulation of fat that can be harmful to health. These are conditions that can lead to a lifetime of diseases, including cancer, diabetes, and heart disease. The diagnosis of overweight and obesity in children and adolescents depends on the international reference used. (2) Objectives: In this study aimed to determine the level of concordance of the prevalence of underweight, normal weight, overweight, and obesity were estimated with three international references in Mexican children and adolescents between 2 and 18 years of age. (3) Methods: We used specific the body mass index (BMI) cut-off points of the ENSANUT Continua 2022 database. The weight category was measured using the World Health Organization (WHO) criteria (<−2 standard deviation (SD), underweight; −2 and +0.99 SD, normal weight; +1 and +1.99 SD, overweight; and ≥+2 SD, obesity); Centers for Disease Control and Prevention (CDC) criteria (<percentile 5, underweight; percentile 5 and <percentile 85, normal weight; ≥percentile 85, overweight; and ≥percentile 95, obesity); and the Obesity Task Force (IOTF) criteria (specific limit values). To determine agreement among these three criteria, Cohen’s Kappa index was used. (4) Results: There were differences in the estimation of weight categories according to the international reference used. Substantial (WHO-IOTF: 0.639; CDC-IOTF: 0.785) and almost perfect (WHO-CDC: 0.806) levels of agreement were found between the references used; however, agreement varied according to age. (5) Conclusions: The weight category depends on the reference used, so each one should be used with caution since the results inform our actions of prevention, surveillance, and the control of nutrition in childhood and adolescence for the timely detection of chronic health problems and effects of social deficiencies. Full article

Given the increasing number of phenomena that demand interpretation and investigation, developing new distributions and families of distributions has become increasingly essential. This article introduces a novel family of distributions based on the exponentiated reciprocal of the hazard rate function named the new Lomax-G family of distributions. We demonstrate the family’s flexibility to predict a wide range of lifetime events by deriving its cumulative and probability density functions. The new Lomax–Weibull distribution (NLW) is studied as a sub-model, with analytical and graphical evidence indicating its efficiency for reliability analysis and complex data modeling. The NLW density encompasses a variety of shapes, such as symmetrical, semi-symmetrical, right-skewed, left-skewed, and inverted J shapes. Furthermore, its hazard function exhibits a broad range of asymmetric forms. Five estimation techniques for determining the parameters of the proposed NLW distribution include the maximum likelihood, percentile, least squares, weighted least squares, and Cramér–von Mises methods. The performance of the estimators of the studied inferential methods is investigated through a comparative Monte Carlo simulation study and numerical demonstration. Additionally, the effectiveness of the NLW is validated by means of four real-world datasets. The results indicate that the NLW distribution provides a more accurate fit than several competing models. Full article

Self-reported occupational exposure was previously associated with COPD in the Spanish population. This study aimed to analyse the relationship between occupational exposure to various chemical and biological agents, COPD, emphysema, and the bronchial wall area, which was determined by lung computed tomography (CT) in 226 individuals with COPD and 300 individuals without COPD. Lifetime occupational exposures were assessed using the ALOHA(+) job exposure matrix, and CT and spirometry were also performed. COPD was associated with high exposure to vapours, gases, dust and fumes (VGDF) (OR 2.25 95% CI 1.19–4.22), biological dust (OR 3.01 95% CI 1.22–7.45), gases/fumes (OR 2.49 95% CI 1.20–5.17) and with exposure to various types of solvents. High exposure to gases/fumes, chlorinated solvents and metals (coefficient 8.65 95% CI 1.21–16.09, 11.91 95%CI 0.46- 23.36, 14.45 95% CI 4.42–24.49, respectively) and low exposure to aromatic solvents (coefficient 8.43 95% CI 1.16–15.70) were associated with a low 15th percentile of lung density indicating emphysema. We conclude that occupational exposure to several specific agents is associated with COPD and emphysema in the Spanish population. Full article