Search Results (140)

This study proposes a novel measurement system repeatability and reproducibility (R&R) framework for zero-inflated correlated defect count data in semiconductor wafer automated optical inspection (AOI). In advanced semiconductor manufacturing environments, AOI systems are extensively used to detect wafer defects such as particles, scratches, and structural abnormalities. However, conventional Gauge R&R methods are primarily developed for continuous Gaussian-type measurements and are therefore not fully appropriate for high-yield semiconductor inspection data characterized by discrete defect counts, excessive zero observations, and correlated defect categories. To address these limitations, this study develops a zero-inflated bivariate Poisson (ZIBP) measurement system model capable of simultaneously capturing correlated defect generation mechanisms and structural zero-defect states. A latent variable representation is introduced to model shared and category-specific defect sources, while a zero inflation mechanism accounts for defect-free wafer observations commonly encountered in precision manufacturing. An expectation–maximization (EM) algorithm is further developed for parameter estimation, including latent common defect counts and structural zero probabilities. Based on the fitted model, the repeatability variance, reproducibility variance, total measurement variation, and Percent R&R are estimated under the proposed probabilistic framework. In addition, bootstrap resampling is employed to construct confidence intervals for the proposed R&R measures. Theoretical properties of the proposed framework, including covariance structure, identifiability, EM monotonicity, estimator consistency, and asymptotic behavior of the Percent R&R estimator, are analytically established. The proposed framework extends traditional Gauge R&R analysis from continuous Gaussian measurements to zero-inflated correlated count-type defect inspection data and provides a statistically rigorous methodology for evaluating AOI measurement system reliability in semiconductor wafer manufacturing environments. Full article

Invasive alien species (IAS) threaten coastal wetland ecosystems, and smooth cordgrass (Spartina alterniflora) is among the most damaging invaders along the coast of China. We compiled occurrence records from the invaded range (China) and native range (United States) and retained 358 and 291 spatially thinned occurrences after quality control and definition of coastal-accessible areas. We assembled climatic, topographic, land use, soil and anthropogenic predictors and fitted species distribution models using the biomod2 ensemble-modeling framework, complemented by an ecospat-based comparison of native and invaded niche spaces. The ensemble model (EM) showed high predictive accuracy (China: AUC = 0.98, TSS = 0.99; USA: AUC = 0.99, TSS = 0.94). Elevation (73.6%) and human influence (6.0%) were the strongest predictors, highlighting the role of intertidal geomorphology and human-mediated propagule pressure. Niche overlap between ranges was low (Schoener’s D = 0.13), and the invaded niche showed substantial unfilling (0.36), indicating additional environmental space at risk of colonization in China. The current suitable habitat forms a continuous coastal belt from the Bohai Rim through the Yellow Sea–East China Sea to the South China Sea. Projections under future climate change suggest predominantly stable suitable areas with localized expansions but potential contractions in some periods. Our results may support the early warning, surveillance prioritization, and adaptive management of S. alterniflora under climate change. Full article

The Poisson exponentially weighted moving average (PEWMA) control chart rests upon the equidispersion assumption of the pure Poisson distribution, a structural symmetry condition stipulating that the process mean and variance are equal. In manufacturing environments characterized by latent process heterogeneity, this assumption is systematically violated: the resulting distributions are inherently asymmetric, heavily right-skewed, and overdispersed. This structural asymmetry renders standard PEWMA control limits artificially narrow, inducing a substantial inflation of false alarm rates. This paper introduces the Poisson mixture EWMA (PM-EWMA) control chart, which models the latent heterogeneous structure of count data as a finite Poisson mixture distribution, with parameters estimated via the Expectation–Maximization (EM) algorithm without requiring prior labeling of process states. The optimal number of components is determined via the Bayesian Information Criterion (BIC) as the primary criterion, supplemented by the Akaike Information Criterion (AIC), its bias-corrected variant (AICc), and the log-likelihood ratio diagnostic. The PM-EWMA chart incorporates the exact mixture variance, accounting for both within-component and between-component variability, into the EWMA control limit structure, thereby providing a theoretically justified correction under the fitted Poisson mixture assumption. A Monte Carlo simulation study comprising 495 factorial configurations benchmarks the PM-EWMA chart against both the standard PEWMA chart and the negative binomial EWMA (NB-EWMA) chart with oracle dispersion calibration, confirming stable in-control ARL performance and demonstrating improved discrimination relative to the misspecified PEWMA baseline. Empirical validation using fabric defect count data from two textile manufacturers in Türkiye, with Overdispersion Indices of 6.01 and 2.74, respectively, demonstrates false alarm reductions ranging from 40.9% to 89.2% relative to the standard PEWMA chart, depending on the smoothing parameter and degree of overdispersion. Full article

Wind dynamics in urban environments exhibit non-stationarity and marked spatial variability, complicating stochastic modeling when a single global distribution is assumed. This article discusses the estimation of wind density under quasi-stationary regimes at the local level using SICABI, a two-phase framework: (i) Stationary Region Identification (ISR) estimates, through spectral power analysis, a specific dominant period for each location and validates the induced subsampling using the Augmented Dickey–Fuller (ADF) test, and (ii) RAPID adjusts an adaptive parametric density by recursively updating the mixture parameters and creating new components when a normalized membership distance exceeds a threshold. The analysis uses wind speed records collected from eight stations in the Metropolitan Area of Queretaro, Mexico, during the period from 1 January 2023 to 31 December 2023, aggregated at a 10 min resolution, from which $X_{\delta ,s}$ is constructed for each site. RAPID is compared against Gaussian Kernel Density Estimation (KDE) with Silverman bandwidth and EM-fitted Gaussian mixtures with BIC-based selection ($K_{max}=12$). The resulting densities were compared with an empirical density estimated from a histogram over a fixed grid ($m=50$) using the MISE and RMSE metrics. The results reveal marked site-dependent differences in dominant periodicity and residual behavior, including asymmetry and heavy tails. ISR identified dominant periods ranging from 37 to 166 days, and RAPID adapted its complexity with $K_s\in [5,10]$ without fixing the number of mixture components in advance. Quantitatively, RAPID achieved the lowest RMSE at 6/8 sites and the lowest MISE at 5/8 sites, while also exhibiting shorter execution times than KDE and MoG under the same input $X_{\delta ,s}$. The results support RAPID as a competitive adaptive method for site-specific density estimation in non-stationary urban climate signals. In this context, local regimes can be viewed as approximate invariants under time translation in the weak stochastic sense, while deviations from this assumption are reflected in increased distributional complexity across sites. Full article

In the insurance literature, accurately predicting extreme losses has been a persistent and important problem. Recently, under the modelling framework of weighted distributions, several finite-mixture size-biased distributions, including size-biased Weibull and size-biased truncated log-normal distributions, have gained popularity for modelling heavy-tailed insurance claim data. In this study, unlike existing models, we explicitly account for the individual heterogeneity commonly observed in insurance claims by treating the order of size-biased weighting as a continuous latent variable, thereby constructing a mixed size-biased distribution. In particular, we study the various distributional properties of the mixed log-normal distribution with a truncated normal prior, which serves as a conjugate prior for the size-biased log-normal model. For applications in non-life insurance, we discuss the Bayesian credibility premium and present an estimation of a regression model via the EM algorithm. We further conduct a real-data analysis using insurance loss data, comparing goodness-of-fit and tail risk measures with those of standard heavy-tailed distributions. Full article

Background/Objectives: There is high demand for Anshenbunao syrup (ABS) in Chinese medicine owing to its steady therapeutic efficacy for insomnia and neurasthenia. However, it contains a substantial proportion of Polygoni Multiflori Radix Praeparata (PMRP), which is associated with reported cases of drug-induced liver injury (DILI). Here, we aim to establish an integrated approach combining PK screening with a dual-model toxicity verification system to systematically identify liver injury components (from high to low concentrations and from direct to idiosyncratic hepatotoxicity) to accurately uncover diverse potential hepatotoxicity markers. Methods: A sensitive UPLC-MS/MS method was used to accurately quantify the components in plasma at the ng/mL level and conduct a pharmacokinetic analysis. Rat models were used to evaluate exposure levels of the eight active constituents and three major metabolites after a single oral gavage dose of 10 mL/kg ABS and identify the quality markers. The early-stage and high-throughput assessment of direct and idiosyncratic hepatotoxicity was conducted in vitro utilizing HepG2 cells. After the administration of the quality markers (0.01–80 μM), CCK-8 was used to detect cell viability on both normal and susceptible cells, and the latter was induced by lipopolysaccharide. Results: As a result, seven quality markers were screened based on their contents and exposure levels in rat plasma by UPLC–MS/MS, including emodin (EM), liquiritin (LI), 2,3,5,4′–Tetrahydroxystilbene–2–O–β–D–glucoside (TSG), icariin, emodin–8–O–β–D–glucoside, baohuoside I (BA), and 18β–glycyrrhetinic acid (GTA). Moreover, the half maximal inhibitory concentration values of both normal cells and the lipopolysaccharide-induced immune stress liver injury cells were fitted within the concentration range of 0.01–80 μM, based on which, EM, BA, and GTA were identified as the principal hepatotoxic constituents in ABS at elevated concentrations. This study is the first to demonstrate that TSG, EM, LI, and GTA exhibit synergistic cytotoxicity in LPS-sensitized hepatocytes at clinically relevant concentrations, whereas EM was also a direct hepatotoxic component. Given that TSG is one of the major ingredients in ABS, the underappreciated idiosyncratic hepatotoxicity could elevate the risk of adverse clinical outcomes. Conclusions: In conclusion, this study effectively identifies hepatotoxic constituents in ABS and evaluates their hazards under immune stress and toxicity profiles in clinical concentrations, which also provides a robust foundation for the awareness of PMRP-induced DILI due to ABS. Full article

The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs. Full article

Accurate monitoring of income inequality is critical for sustainable socioeconomic development and realizing the United Nations Sustainable Development Goals (SDGs). However, assessing inequality for counties continues to be challenging because of the high cost of household surveys and the limited accuracy of traditional nighttime light (NTL) proxies. To address this gap, we develop the Distribution Matching-based Individual Income Inequality Estimation Model (DM-I3EM), which integrates NTL data with household surveys. The model employs a three-stage workflow: logarithmic transformation of NTL data, estimation of Gini coefficients through Weibull distribution fitting, and selection of region-specific regression models, enabling high-resolution mapping and spatiotemporal analysis of county-level income inequality across China. Results show that DM-I3EM achieves superior performance, with an R² of 0.76 in China’s Eastern region (outperforming conventional NTL-based methods, R ≈ 0.5). By overcoming the spatiotemporal gaps of survey data, the model enables full-coverage estimation, revealing a regional divergence in income inequality across China from 2013 to 2022: inequality is intensifying in northern and western counties while stabilizing in the developed southern coastal regions. Furthermore, spatial agglomeration of inequality has strengthened, particularly in coastal urban clusters. These findings highlight emerging risks to socioeconomic sustainability. This study provides a robust, replicable framework for estimating inequality in data-scarce regions, offering policymakers actionable evidence to identify high-risk areas and design targeted strategies for advancing SDG 10 (Reduced Inequalities). Full article

This study specifies and validates a three-layer Structural Equation Model (SEM) that accounts for how tourists’ evaluations of destination attributes translate into loyalty; the model is based on UN Tourism’s sustainability pillars. Guided by service-science and Customer Experience (CX) logics, and adopting a Tourist Experience (TX) framework that treats Tourist Experience as a domain-specific case of CX, we define five first-order antecedents—Emotions (EMS), Local Culture (CTL), Authenticity (AUT), Entertainment (ENT), and Servicescape (SVS)—that load onto a higher-order appraisal, Global Perception (GEN), which in turn drives Destination Loyalty (LOY). Using ordinal indicators and a robust diagonally weighted least squares estimator (WLSMV), the model exhibits a good global fit (CFI/TLI = 0.970/0.968; SRMR = 0.049; RMSEA = 0.073 [90% CI = 0.070–0.076]). Standardized effects indicate that GEN is primarily explained by Emotions (β = 0.445, p < 0.001), Authenticity (β = 0.271, p < 0.001), and Servicescape (β = 0.241, p < 0.001), whereas CTL and ENT are not significant when competing with these other predictors. GEN strongly predicts LOY (β = 0.967, p < 0.001), mediating sizable indirect effects from EMS, AUT, and SVS to LOY. The findings corroborate a parsimonious mediational chain in which affective, meaning-related, and infrastructural inputs cohere into a single global appraisal that is proximal to loyalty. Our study provides a decision-focused blueprint for designing emotion-rich, authenticity-protecting, and well-orchestrated servicescapes to enhance GEN and, consequently, LOY; it adheres to established SEM reporting standards and articulates a holistic transactional conceptualization grounded in recent tourism literature. Improvements in GEN reflect not only better experiences but also designs consistent with long-run destination sustainability. Full article

This in vitro investigation evaluated the marginal fit of three pressable glass-ceramic onlay materials: a conventional monolithic lithium disilicate (IPS e.max Press, EM, ivoclar vivadent AG, Schaan, Liechtenstein) and two zirconia-reinforced glass-ceramics (Vita Ambria, VA, VITA Zahnfabrik, Bad Säckingen, Germany; Celtra Press, CP, Sirona Dentsply, Milford, CT, USA). A typodont maxillary first premolar was prepared for an intensive onlay design by a single operator using a milling surveyor. The master die was duplicated with silicone impressions to create 72 identical epoxy resin dies. Seventy-two onlays (n = 24 per material) were fabricated and adhesively cemented to their respective dies. Vertical marginal gaps were recorded under a stereo-electron microscope before and after thermomechanical loading (TML) in a chewing simulator. Data were analyzed with one-way ANOVA and Tukey’s post hoc tests for intergroup comparisons and paired t-tests for pre- versus post-TML values. All groups showed a significant increase in marginal gap following TML. VA exhibited mean gaps of 46.41 µm before and 57.28 µm after loading (p = 0.001). EM demonstrated 41.16 µm before and 46.63 µm after TML (p = 0.002). CP showed 45.70 µm before and 55.99 µm after TML (p = 0.003). Among the three materials, EM maintained the most accurate marginal adaptation both before and after simulated chewing. Despite the increases, all post-loading values remained within the clinically acceptable threshold for marginal discrepancy. These findings indicated that thermomechanical fatigue adversely affected the marginal integrity of pressable glass-ceramic onlays, including zirconia-reinforced formulations. Nevertheless, zirconia-reinforced ceramics (VA and CP) achieved marginal gaps comparable to conventional lithium disilicate and remained within acceptable clinical limits. IPS e.max Press provided the best overall fit, suggesting it may offer superior long-term marginal stability for onlay restorations. Full article

NMOSFET, whose gate is on the top of the n-p-n junction with gate oxide in between, is called the n-channel transistor. This bipolar junction underneath the gate oxide may provide an n-n-n-conductive channel as the gate is applied with a positive bias over the threshold voltage (V_th). Conceptually, the definition of an n-type or p-type semiconductor depends on whether the corresponding Fermi energy is higher or lower than the intrinsic Fermi energy, respectively. The positive bias applied to the gate would bend down the intrinsic Fermi energy until it is lower than the original p-type Fermi energy, which means that the p-type becomes strongly inverted to become an n-type. First, the thickness of the inversion layer is derived and presented in a planar 40 nm MOSFET, a 3D 240 nm FinFET, and a power discrete IGBT, with the help of the p (1/m³) of the p-type semiconductor. Different ways of finding p (1/m³) are, thus, proposed to resolve the strong inversion layers. Secondly, the conventional formulas, including the triode region and saturation region, are already modified, especially in the triode region from a continuity point of view. The modified formulas then become necessary and available for fitting the measured characteristic curves at different applied gate voltages. Nevertheless, they work well but not well enough. Thirdly, the electromagnetic wave (EM wave) generated from accelerating carriers (radiation by accelerated charges, such as synchrotron radiation) is proposed to demonstrate phonon scattering, which is responsible for the Source–Drain current reduction at the adjoining of the triode region and saturation region. This consideration of reduction makes the fitting more perfect. Fourthly, the strongly inverted layer may be formed but not conductive. The existing trapping would stop carriers from moving (nearly no mobility, μ) unless the applied gate bias is over the threshold voltage. The quantum confinement addressing the quantum well, which traps the carriers, is to be estimated. Full article

This paper aims to improve the accuracy of reliability estimates and the failure time prediction for products exhibiting nonlinear degradation behavior under constant-stress accelerated degradation test (CSADT). To achieve this, a novel degradation model and a life prediction method are proposed, which are based on a generalized Wiener process. Some models assume that the drift coefficients are related to accelerated stress. However, in certain applications, the diffusion coefficients are also affected by accelerated stress. The relationship between the drift parameter and accelerated stress variables can be derived by the accelerated model, and so is the relationship between the diffusion parameter and stress variables based on the principle of invariance of the acceleration factor. To account for individual variability among products, random effects are introduced. Model parameters are estimated using a combination of maximum likelihood estimation (MLE) and the expectation-maximization (EM) algorithm. Furthermore, the probability density function (PDF) of the remaining useful life under normal stress conditions is derived using the law of total probability. The effectiveness and applicability of the proposed approach are validated using simulated constant stress accelerated degradation data and stress relaxation data. The results demonstrate that the model not only fits the degradation process well but also modestly improves the accuracy of the failure time prediction, providing valuable guidance for engineering maintenance and reliability management. Full article

This paper introduces the UG-EM (Unconditional Gamma-Exponential Model) as a new compound lifetime model designed to enhance flexibility in tail behavior compared to traditional distributions. The UG-EM model provides a unified framework for analyzing deviations from symmetry in survival data, effectively capturing right-skewed patterns, which are commonly observed in real-world lifetime phenomena. The main analytical properties are derived, including the probability density, cumulative distribution, hazard and reversed-hazard functions, mean residual life, and several measures of dispersion and uncertainty. The effects of the UG-EM parameters (α and λ) are examined, showing that increasing either parameter can cause a temporary reduction in entropy H(T) at early times followed by a long-term increase; in some cases, the influence of α is stronger than that of λ. Parameter estimation is carried out using the maximum likelihood method and assessed through Monte Carlo simulations to evaluate estimator bias and variability, highlighting the significant role of sample size in estimation accuracy. The proposed model is applied to three survival datasets (Lung, Veteran, and Kidney) and compared with classical alternatives such as Exponential, Weibull, and Log-normal distributions using standard goodness-of-fit criteria. Results indicate that the UG-EM model offers superior flexibility and can capture patterns that simpler models fail to represent, although the empirical results do not demonstrate a clear, consistent superiority over standard competitors across all tested datasets. The paper also discusses identifiability issues, estimation challenges, and practical implications for reliability and medical survival analysis. Recommendations for further theoretical development and broader model comparison are provided. Full article

EMFIT, also referred to as EMFi, is a ferroelectret film related to polyvinylidene fluoride (PVDF) sensors. It is an electroactive polymer (EAP) based on a polyolefin structure and consists of three layers of polyester film. Its application in geophysical monitoring has not been reported in the literature. At present, EMFIT is mainly employed in ballistocardiography and medical sleep monitoring, as developed by the manufacturer Emfit Ltd. (Vaajakoski, Finland). Within the multidisciplinary monitoring network of the National Institute for Earth Physics (NIEP), EMFIT is used as a pressure sensor in combination with infrasound transducers and microphones deployed in seismic areas. The primary aim of this study is to evaluate its suitability for detecting seismic noise that precedes earthquakes, generated by rock fracturing associated with crustal deformation. Although similar studies have been reported, they have not involved the use of EMFIT sensors. The novelty of this approach lies in the large surface area and mechanical flexibility of the material. Beyond seismic forecasting, the research also examines whether this type of sensor can contribute to seismic monitoring as a complement to conventional instruments such as accelerometers, seismometers, and microbarometers. Data analysis relies primarily on spectral time-series methods and incorporates measurements from other acoustic sensors (microphones and microbarometers) as well as a weather station. The working hypothesis is the potential correlation between the recorded data and the presence of enhanced noise prior to the detection of seismic waves by standard seismic sensors. The target area for this investigation is Vrancea, specifically the Vrâncioaia seismic station, where multidisciplinary monitoring includes infrasound, radon, thoron, soil temperature, and atmospheric electrical discharges. Preliminary tests suggest that the EMFIT sensor may function as a highly sensitive device, effectively serving as an “ear” for detecting ground noise. Full article

Background/Objectives: This study aimed to assess the anthropometric and body composition characteristics, blood profiles and fitness-related parameters of emergency medical services (EMS) professionals. Methods: A total of 39 EMS professionals participated in the study, consisting of 18 males (age: 37.78 ± 10.62 years, height 180.87 ± 6.00 cm, weight 105.42 ± 25.40 kg) and 21 females (age: 33.05 ± 7.44 years, height 167.29 ± 3.89 cm, weight 90.63 ± 21.20 kg). The testing included anthropometric measurements, blood profiling, handgrip and low back strength assessments, vertical jump evaluation, flexibility testing, sit-up and push-up assessments, as well as timed performance measurements for a 300 m sprint and a 1.5-mile run. Results: According to the body mass index (BMI), 41% of the EMS professionals were classified as obese. Self-reported data indicated a smoking prevalence of 23%, while diabetes (n = 3), asthma (n = 2), and hypertension (n = 2) were also reported. Our results reveal that a great proportion of EMS professionals demonstrated suboptimal body composition and performance levels, with many failing to meet recommended health and performance standards. Elevated BMI, waist and hip circumferences, and body fat percentages were observed, along with relatively low performance in the strength and endurance tests. Conclusions: These findings highlight the need for targeted interventions among EMS professionals, who are expected to maintain adequate levels of aerobic capacity, flexibility, muscular strength and endurance. Full article