Search Results (931)

Substance use is a critical public health issue in the U.S., with Black communities, particularly those with criminal justice contact, disproportionately affected. Chronic exposure to stressors can lead to substance use as a coping strategy. This study used data from 1476 Black adults with criminal justice involvement from the National Survey of American Life to examine how psychosocial stress and sleep disturbances relate to lifetime substance use and to determine if there are any sex differences. Sex-separate generalized linear models for a Poisson distribution with a log-link function estimated prevalence ratios and adjusted prevalence ratios (APRs) for lifetime alcohol abuse, lifetime cigarette, and marijuana use. Independent variables include stressors (family, person, neighborhood, financial, and work-related) and sleep problems, with covariates such as age, SES, and marital status. Lifetime alcohol abuse was associated with family stressors (APR = 2.72) and sleep problems (APR = 3.36) for males, and financial stressors (APR = 2.75) and sleep problems (APR = 2.24) for females. Cigarette use was linked to family stressors (APR = 1.73) for males and work stressors (APR = 1.78) for females. Marijuana use was associated with family stressors (APR = 2.31) and sleep problems (APR = 2.07) for males, and neighborhood stressors (APR = 1.72) for females. Lifetime alcohol abuse, as well as lifetime cigarette and marijuana use, was uniquely associated with various psychosocial stressors among Black adult males and females with criminal justice contact. These findings highlight the role of structural inequities in shaping substance use and support using a Social Determinants of Health framework to address addiction in this population. Full article

We propose the zero-inflated Polynomially Adjusted Poisson (zPAP) model. It extends the usual zero-inflated Poisson by multiplying the Poisson kernel with a nonnegative polynomial, enabling the model to handle extra zeros, overdispersion, skewness, and even multimodal counts. We derive the maximum-likelihood framework—including the log-likelihood and score equations under both general and regression settings—and fit zPAP to the zero-inflated, highly dispersed Fish Catch data as well as a synthetic bimodal mixture. In both cases, zPAP not only outperforms the standard zero-inflated Poisson model but also yields reliable inference via parametric bootstrap confidence intervals. Overall, zPAP is a clear and tractable tool for real-world count data with complex features. Full article

The time fluctuations of forest fires occurring in Basilicata, a region situated in Southern Italy, between 2004 and 2023 were investigated using various analytical approaches. Analysis revealed a clustering of fire occurrences over time, as indicated by a significantly high coefficient of variation. This suggests that the fire sequence does not follow a Poisson distribution and instead exhibits a clustered structure, largely driven by the heightened frequency of events during the summer seasons. The analysis of monthly forest fire occurrences and total burned area indicates a significant correlation between the two. This correlation is reinforced by shared patterns, notably an annual cycle that appears to be influenced by meteorological factors, aligning with the yearly fluctuations in the region’s weather conditions typical of a Mediterranean climate. Furthermore, the relationship between the Standardized Precipitation Evapotranspiration Index (SPEI) and forest fires revealed that the accumulation period of the SPEI corresponds to the cycle length of the fires: longer cycles in fire occurrences align with higher accumulation periods in SPEI data. Full article

To address the issue of the low compression–tension ratio in the traditional parallel bond model (PBM), this study proposes an improved PBM incorporating a random distribution strategy of strong–weak contact groups. An L₂₇(3¹²) orthogonal experimental design was employed to construct 27 sets of numerical simulation schemes. Combined with Pearson correlation coefficient analysis and multivariate regression, the influence of twelve microscopic parameters on seven of the macroscopic mechanical properties of sandstone was systematically investigated, including elastic modulus (E), Poisson’s ratio (v), uniaxial compressive strength (σ_c), internal friction angle (φ), cohesion (c), crack damage stress ratio (σ_cd/σ_c), and compressive–tensile strength ratio (σ_c/σ_t). Based on these analyses, a quantitative relationship model between the macro and micro parameters was established and validated through numerical simulation and experimental comparison. The proposed method provides a theoretical foundation for the mechanical modeling of sandstone and the inversion of microscopic parameters. Full article

One of the main motivations of this paper is to construct generating functions for generalization of the Touchard polynomials (or generalization exponential functions) and certain special numbers. Many novel formulas and relations for these polynomials are found by using the Euler derivative operator and functional equations of these functions. Some novel relations among these polynomials, beta polynomials, Bernstein polynomials, related to Binomial distribution from discrete probability distribution classes, are given. Full article

A meshless, quasi-convex reproducing kernel particle framework for three-dimensional steady-state thermomechanical coupling problems is presented in this paper. A meshfree, second-order, quasi-convex reproducing kernel scheme is employed to approximate field variables for solving the linear Poisson equation and the elastic thermal stress equation in sequence. The quasi-convex reproducing kernel approximation proposed by Wang et al. to construct almost positive reproducing kernel shape functions with relaxed monomial reproducing conditions is applied to improve the positivity of the thermal matrixes in the final discreated equations. Two numerical examples are given to verify the effectiveness of the developed method. The numerical results show that the solutions obtained by the quasi-convex reproducing kernel particle method agree well with the analytical ones, with a slightly better-improved numerical accuracy than the element-free Galerkin method and the reproducing kernel particle method. The effects of different parameters, i.e., the scaling parameter, the penalty factor, and node distribution on computational accuracy and efficiency, are also investigated. Full article

This work explores the physical properties of the MAX-phase material Cr₃AlC₂ through the application of density functional theory (DFT). The refined lattice parameters were determined through the minimization of the total energy. In order to explore the electronic properties and bonding features, we carried out computations on the band structure and charge density distribution. The calculated elastic constants (C_ij) validated the mechanical stability of Cr₃AlC₂. To assess the material’s ductility or brittleness, we calculated Pugh’s ratio, Poisson’s ratio, and Cauchy pressure. The hardness was determined. This study examined the anisotropic behavior of Cr₃AlC₂ using directional analyses of its elastic properties and by computing relevant anisotropy indicators. We examined several key properties of Cr₃AlC₂, including the Grüneisen parameter, acoustic characteristics, Debye temperature, thermal conductivity, melting point, heat capacity, Helmholtz free energy, entropy, and internal energy. Phonon dispersion spectra were analyzed to assess the dynamic stability of Cr₃AlC₂. Full article

Background: Clostridioides difficile infection (CDI) is a major concern in hospital-acquired infections. C. difficile spores can survive on surfaces for months and require sporicidal disinfection for elimination. The use of disinfectants should be based on laboratory-confirmed sporicidal activity, tested according to current standards in suspension and carrier tests. Further evaluation of disinfectant efficacy should occur in clinical settings by analyzing reductions in CDI incidence. This study aims to conduct a retrospective analysis of the impact of a new disinfection protocol and concurrent changes in antibiotic consumption on the incidence of healthcare-acquired CDI (HA-CDI). Methods: This retrospective, single-center study assessed the impact of a chlorine dioxide-based disinfection protocol on HA-CDI across three periods: pre-intervention, intervention, and post-intervention. An interrupted time series analysis (ITS) with a Poisson distribution was used to evaluate the incidence of HA-CDI, while antibiotic consumption data were analyzed to identify any correlation with CDI infection rates. Results: Incidence Rate Ratio (IRR) before the intervention is 1.00, serving as the reference value. During the intervention period, the IRR is 0.79 (95% CI: 0.42–1.36; p = 0.43), indicating a decrease in the incidence of infections compared to the pre-intervention period, although this result is not statistically significant. After the intervention, the IRR is 0.53 (95% CI: 0.26–0.97; p = 0.057), suggesting a further reduction in the incidence of CDI; this result is on the borderline of statistical significance (p = 0.057), indicating a potential effect of the intervention, albeit without full statistical certainty. Conclusions: The absence of a CDI surge despite increased antibiotic consumption highlights the synergistic relationship between antibiotic stewardship and rigorous infection control practices. The combination of the improved disinfection protocol and comprehensive staff training proved remarkably effective in mitigating CDI risk. Cleaning and disinfection in healthcare facilities is crucial for the prevention of healthcare-associated infections. Full article

With the advancement of intelligent transportation systems, vehicle trajectory data have become a key component in areas like traffic flow prediction, route planning, and traffic management. However, high-quality, publicly available trajectory datasets are scarce due to concerns over privacy, copyright, and data collection costs. The lack of data creates challenges for training machine learning models and optimizing algorithms. To address this, we propose a new method for generating synthetic vehicle trajectory data, leveraging traffic flow characteristics and road maps. The approach begins by estimating hourly traffic volumes, then it uses the Poisson distribution modeling to assign departure times to synthetic trajectories. Origin and destination (OD) distributions are determined by analyzing historical data, allowing for the assignment of OD pairs to each synthetic trajectory. Path planning is then applied using a road map to generate a travel route. Finally, trajectory points, including positions and timestamps, are calculated based on road segment lengths and recommended speeds, with noise added to enhance realism. This method offers flexibility to incorporate additional information based on specific application needs, providing valuable opportunities for machine learning in intelligent transportation systems. Full article

This study examines a single-server Markovian queueing system featuring continuous service and an infinite number of customers at both ends—namely, offline and online clients. Offline customers are conventional clients who arrive at the system following a Poisson process, while online customers are assumed to be endlessly present in the system. All service times are exponentially and identically distributed and independent. Utilizing generating functions and Laplace transform techniques, this study derives exact analytical expressions for the system size probabilities in both transient and steady states. Furthermore, it evaluates key performance measures for each state and provides graphical representations to illustrate the system’s dynamics, thereby enriching the understanding of its operational behavior. This work contributes to the advancement of priority-based queueing models and proposes a novel framework applicable to hybrid service architectures in contemporary digital ecosystems. Full article

Wellbore strengthening is a widely applied technique to mitigate wellbore leakage during drilling operations in complex formations characterized by narrow mud weight windows. This method enhances the wellbore’s pressure-bearing capacity by using lost circulation materials (LCMs) to bridge natural or induced fractures. In recent years, advanced sealing technologies such as wellbore reinforcement have gradually been applied and developed, but their related influencing factors and mechanisms have not been deeply revealed. This article uses the Cohesive module of ABAQUS to establish a wellbore fracture sealing model. By establishing a porous elastic finite element model, the elastic mechanics theory of porous media is combined with finite element theory. Under the influence of factors such as anisotropy of geostress, reservoir elastic modulus, Poisson’s ratio, and fracturing fluid viscosity, the circumferential stress distribution of the wellbore after fracture sealing is simulated. The simulation results show that stress anisotropy has a significant impact on Mises stress. The greater the stress anisotropy, the more likely the wellbore sealing is to cause wellbore rupture or instability. Therefore, it is necessary to choose a suitable wellbore direction to avoid high stress concentration areas. The elastic modulus of the reservoir is an important parameter that affects wellbore stability and fracturing response, especially in high modulus reservoirs where the effect is more pronounced. Poisson’s ratio has a relatively minor impact. In fracturing and plugging design, the viscosity of fracturing fluid should be reasonably selected to balance the relationship between plugging efficiency and wellbore mechanical stability. In the actual drilling process, priority should be given to choosing the wellbore direction that avoids high stress concentration areas to reduce the risk of wellbore rupture or instability induced by plugging, specify targeted wellbore reinforcement strategies for high elastic modulus reservoirs; using models to predict fracture response characteristics can guide the use of sealing materials, achieve efficient bridging and stable sealing, and enhance the maximum pressure bearing capacity of the wellbore. By simulating the changes in circumferential stress distribution of the wellbore after fracture sealing, the mechanism of wellbore reinforcement was explored to provide guidance for mechanism analysis and on-site application. Full article

Background/Objectives: In the aftermath of the COVID-19 pandemic, individuals infected with SARS-CoV-2 have progressively displayed a range of symptoms linked to protracted COVID during the post-acute phase of illness. Concurrently, in several nations globally, the phenomenon of population aging has been intensifying. In this scenario, the aged population has become both vulnerable and high-risk during the acute phase of COVID-19, and faces significant dangers associated with long-COVID. This study seeks to analyze the incidence and spatial distribution of health complications in older people affected by COVID-19, in the first year of the pandemic (2020), in the State of Paraná, as well as to identify the factors associated with the development of cardiovascular, neurological, respiratory, and metabolic diseases. Method: An observational and retrospective study was carried out in the Brazilian state of Paraná. Participants were randomly selected from two databases. A total of 893 older people (≥60 years) participated in the study 12 months after acute COVID-19 infection. Telephone questionnaires were applied between March and December 2021. The Moran index test, logistic regression, and Poisson models were used to analyze the data. Results: In terms of age, most participants (66%) were between 60 and 69 years old, 25.8% were between 70 and 79 years old, and 8.2% were 80 years old or older. Most participants were female (51.2%), white (98.1%), had a partner (69.8%), and had been hospitalized due to COVID-19 (59.3%). Cardiovascular diseases were the most frequent in the population (39.5%), followed by metabolic diseases (27.3%). The long-term use of medication was associated with the development of metabolic diseases (aOR = 9.8), cardiovascular diseases (aOR = 6.6), and diseases in multiple organic systems (aOR = 3.2); living alone was associated with neurological diseases (aOR = 2.5), and the age group of 80 years or older (aOR = 2.4) was associated with cardiovascular events. The spatial distribution showed that complications in body groups are distributed randomly among the health regions of the state, with no influence from neighboring locations. Conclusions: Post-COVID-19 health complications are more frequent in older adults who have comorbidities and long-term medication use. Therefore, long-term monitoring of these individuals and investment in public policies for rehabilitation and prevention of complications are necessary. Full article

It is frequent for real-life count data to show inflation in lower values; however, most of the well-known count distributions cannot capture such a feature. The present paper introduces a new distribution for modeling inflated count data in small values based on a conflation of distributions approach. The new distribution inherits some properties from Poisson distribution (PD) and logarithmic distribution (LD), making it a powerful modeling tool. It can serve as an alternative to PD, LD, and zero-truncated distributions. The new distribution is worth considering theoretically, as it belongs to the weighted PD family. With zero as a support point, two additional models are suggested for the new distribution. These modifications yield distributions that demonstrate overdispersion models comparable to the negative binomial distribution (NBD) while retaining essential PD properties, making them suitable for accurately representing count data with frequent events of low frequency and high variance. Furthermore, we discuss the superior performance of three new distributions in modeling real count data compared to traditional count distributions such as PD and NBD, as well as other discrete distributions. This paper examines the key statistical properties of the proposed distributions. A comparison of the novel and other distributions in the literature is shown employing real-life data from some domains. All of the computations shown in this study are generated using the R programming language. Full article

The problem of estimating the ratio of the means of a two-component Poisson mixture model is considered, when each component is subject to zero-inflation, i.e., excess zero counts. The resulting zero-inflated Poisson mixture (ZIPM) model can be viewed as a three-component Poisson mixture model with one degenerate component. The EM algorithm is applied to obtain frequentist estimators and their standard errors, the latter determined via an explicit expression for the observed information matrix. As an intermediate step, we derive an explicit expression for standard errors in the two-component Poisson mixture model (without zero-inflation), a new result. The ZIPM model is applied to simulated data and real ecological count data of frigatebirds on the Coral Sea Islands off the coast of Northeast Australia. Full article

To investigate the relationship between the seeding performance of a novel pre-cut sugarcane planter designed by South China Agricultural University and operational settings, field seeding tests was conducted with the following protocol: First, the John Deere M1654 tractor’s forward velocity was calibrated, and the planter’s safe loading capacity was determined. Subsequently, eight experimental treatments (A–H) were designed to quantify the relationships between the three performance indicators: seeding density N, the seeding efficiency E and seeding uniformity (coefficient of variation, CV), and three key operational parameters: forward speed of planter v, the discharging sprocket rotational speed n, and the hopper outlet size w. Mathematical models (R20.979) between three key operational parameters with two performance indicators (N, E) was developed through analysis of variance (ANOVA) and regression analysis. The seeding rate per meter was confirmed to follow a Poisson distribution based on Kolmogorov–Smirnov (K–S) tests. When the CV was below 40%, the mean relative error remained within 3%. These findings provide a theoretical foundation for seeding performance prediction under field conditions. Full article