Search Results (8,742)

Pregnant people in U.S. jails experience high rates of opioid use disorder (OUD), yet access to medications for opioid use disorder (MOUD) remains inconsistent. This mixed-methods study examines how jail policies, treatment infrastructure, and political context shape MOUD provision for pregnant incarcerated individuals. We conducted a secondary analysis of a national survey of 2885 U.S. jails (analytic sample = 836). Logistic regression models assessed associations between MOUD provision and telemedicine capacity, community MOUD availability, state Medicaid expansion, and 2020 presidential voting outcomes. Qualitative responses characterized barriers to care. Findings confirm that MOUD access for pregnant incarcerated individuals remains limited and structurally patterned. Fewer than half of jails continued methadone or buprenorphine for pregnant individuals already in treatment, and initiation was uncommon. MOUD provision was more likely in Democrat-won states, jails with telemedicine capacity, and jails located in communities with MOUD providers, while limited community availability reduced odds of provision. Qualitative themes highlighted restrictive jail policies, provider discretion, diversion concerns, and misconceptions regarding fetal harm. These findings underscore persistent structural barriers to evidence-based perinatal OUD treatment in carceral settings and highlight the importance of telemedicine expansion, community treatment capacity, and standardized correctional policies to advance perinatal health equity. Full article

Based on the discrete element method (DEM), a sand particle contact force model and a motion model for the 3D sand printing (3DSP) process were developed. By accounting for the viscous support force and contact force between sand particles, and gravity acting on each individual sand particle, the displacement of sand particles was calculated, enabling the simulation of the 3DSP process using sand particle ensembles. Furthermore, the effects of the ratio of silica sand to ceramsite sand and the recoating speed on sand-recoating performances and mechanical properties were investigated. Irregularly shaped sand particles (primarily silica sand) were constructed via the multi-sphere filling method. The simulation was performed on a virtual sand-recoating device (180 mm in length, 100 mm in width, 70 mm in height) with reference to the EXONE S-MAX printer. Meanwhile, the EXONE S-MAX was utilized to print the bending samples for experimental validation. Simulation and experimental results indicate that as the ratio increases, the porosity first decreases and then increases, whereas mechanical properties exhibit an initial increase followed by a decrease. At a ratio of 3:7, the porosity reaches a minimum of 21.3%; correspondingly, the shear force of bonding bridges peaks at 908 mN, and the bending strength of specimens attains a maximum of 2.87 MPa. With the increasing recoating speed, the porosity rises consistently, while the shear force of bonding bridges and the bending strength of specimens first increase and then decrease, which is primarily attributed to the penetration behavior of the binder under capillary force. At a recoating speed of 160 mm·s⁻¹, the shear force of bonding bridges reaches its maximum, and the specimens achieve a maximum bending strength of 2.89 MPa. The simulation results are well-validated by the experiments. The DEM-based simulation method proposed in this study offers a practical and convenient tool for parameter optimization in 3DSP process. Full article

This study introduces a mesoscale modeling methodology for polyurethane-solidified ballast beds (PSBBs) that eliminates reliance on X-ray computed tomography (XCT) and addresses constraints in specimen size, capital cost, and post-processing complexity. The approach couples the Discrete Element Method (DEM) with the Finite Element Method (FEM). A high-fidelity discrete-element geometry is reconstructed from three-dimensional laser scans of ballast particles. The virtual-ray casting algorithm is then employed to identify the spatial distribution of ballast and polyurethane and map this information onto the finite-element mesh, enabling heterogeneous material reconstruction at the mesoscale. The accuracy of the model and mesh convergence are validated through comparisons with laboratory uniaxial compression tests, determining the optimal mesh size to be 0.4 times the minimum particle size (0.4 D_min). Based on this, a parametric study on the effect of sleeper width on ballast bed mechanical responses is conducted, revealing that when the sleeper width is no less than 0.73 times the ballast bed width (0.73 W_b) an optimal balance between stress diffusion and displacement control is achieved. This method demonstrates excellent cross-material applicability and can be extended to mesoscale modeling and performance evaluation of other multiphase particle–binder composite systems. Full article

In this study, small-scale experiments were performed to examine fuel distribution effects on discrete flame spread behavior under natural convection and ambient wind. To this end, birch rod arrays with regularly varying column number (n) and array spacing (S) were designed. The results indicate that fuel distribution exerts a comparable influence on flame spread under both natural convection and ambient wind conditions. The flame spread rate (V_f), flame length (L_f), and mass loss rate (MLR) are insensitive to changes in S but have an exponential relationship with n. Based on the mass conservation law, prediction correlations for the mass loss rate based on S and n in the stable flame spread stage are proposed. We discovered that nondimensional mass loss has a power law dependence on the fuel coverage rate. In addition, radiative heat transfer dominates the flame spread process for the discrete array. Horizontal flame spread across discrete rod arrays exhibits critical spacing under natural convection. Finally, we established a comprehensive heat transfer model for flame spread under natural convection conditions and obtained a derivation of a critical sustainability criterion for the discrete flame spread process, which considers radiative and convective heat transfer. Full article

Epithelial–mesenchymal plasticity encompasses a spectrum of epithelial and mesenchymal identity states that enable cells to adapt to changing biological contexts. While CD44 isoform usage and epithelial splicing regulators ESRP1/2 are well-characterized in cancer-associated epithelial–mesenchymal transition (EMT), their regulation across physiological, non-transformed identity states remains less well defined. Here, we employed a non-malignant human cellular system comprising primary dermal fibroblasts, induced pluripotent stem (iPS) cells, and iPS-derived mesenchymal stem cells (iPS-MSCs) to define discrete epithelial, intermediate epithelial/mesenchymal, and mesenchymal identity states positioned along an epithelial–mesenchymal identity axis. Morphological assessment, lineage marker profiling, and RT-qPCR analyses revealed reproducible population-level stratification of these states. CD44 expression and alternative splicing followed this hierarchy, with CD44s predominating in fibroblasts, broad variant exon inclusion in iPS cells, and intermediate patterns in iPS-MSCs. ESRP1 expression mirrored CD44 splicing architecture, and ESRP1 silencing in iPS cells induced a shift toward CD44s, confirming its functional contribution to epithelial-associated CD44 splicing. In contrast, Notch-related transcriptional readouts displayed distinct, context-dependent profiles across the examined identity states. Together, this study establishes a tractable non-transformed human model that captures selected molecular features associated with epithelial–mesenchymal plasticity beyond malignant contexts. Full article

A new family of stochastic $\eta$-power log-logistic diffusion processes was introduced and defined based on the classical log-logistic diffusion model. The probabilistic characteristics of the proposed process were derived through an analysis of the associated stochastic differential equation (SDE), including its explicit expressions, the transition probability density function, and the conditional and non-conditional mean functions. The statistical inference of the model was studied, and parameter estimation was performed using the maximum likelihood method based on discrete sampling paths. The proposed probabilistic and statistical framework was applied to data on individuals using the Internet in the United States to assess the practical performance of the model. The empirical results demonstrated that the inclusion of a power in the process improved the goodness of fit compared with the classical formulation, providing better agreement with the observed data. Finally, a small Monte Carlo experiment was performed to examine the robustness of the estimation procedure. Full article

This study aims to propose a fault diagnosis method for partial discharge (PD) in high-voltage (HV) potential transformers (PTs) by combining discrete wavelet transform (DWT), scatter plot (SP), and a residual neural network (ResNet) deep learning model for feature extraction and classification. First, models of HV PTs under normal conditions and three internal fault types were established, including coil eccentricity, voids between the primary winding and the core, and voids between the primary and secondary windings. After measuring the PD signals, DWT filtering was applied to process the signals, and the filtered PD signals, together with the fundamental voltage signals, were transformed into an image-based feature SP to represent the characteristics of each fault. Finally, the SPs were trained using the ResNet model to identify four different defect types in HV PTs. Experimental results showed that the proposed method achieves a fault identification accuracy of 98%. Additionally, compared to other deep learning models, the proposed method significantly improves diagnostic efficiency and accuracy. This study also developed an intelligent online fault monitoring and predictive maintenance system for HV PTs to enhance the stability of power grids and equipment. Full article

To address the challenge of imprecise detection and control of fertilizer application rates caused by high granular flow during fertilization operations, a parallel diversion detection method with real-time application rate regulation is proposed. The mechanism of uniform distribution of discrete particles formed by high-throughput aggregated granular fertilizer was elucidated. Key components including the uniform fertilizer tube, sensor detection structure, six-channel diversion cone disc, and fertilizer convergence tube underwent parametric design, culminating in the innovative development of a six-channel parallel diversion detection device. A multi-channel parallel signal detection method was studied, and a synchronous multi-channel signal acquisition system was designed. Through calibration tests, relationship models were established between the measured flow rate of granular fertilizer and voltage, as well as between the actual flow rate and the rotational speed of the fertilizer discharge shaft. A fuzzy PID control model was constructed in MATLAB2023/Simulink. Using overshoot, response time, and stability as evaluation metrics, the control performance of traditional PID and fuzzy PID was compared and analyzed. To validate the control system’s precision, device performance tests were conducted. Results demonstrated that fuzzy PID control reduced the time required to reach steady state by 66.87% compared to traditional PID, while overshoot decreased from 7.38 g·s⁻¹ to 1.49 g·s⁻¹. Divergence uniformity tests revealed that at particle generation rates of 10, 20, 30, and 40 g·s⁻¹, the coefficient of variation for channel divergence consistency gradually increased with rising tilt angles. During field operations at 0–5.0° tilt, the coefficient of variation for channel divergence consistency remained below 7.72%. Bench tests revealed that the fuzzy PID control system achieved an average accuracy improvement of 3.64% compared to traditional PID control, with a maximum response time of 0.9 s. Field trials demonstrated detection accuracy no less than 92.64% at normal field operation speeds of 3.0–6.0 km·h⁻¹. This system enables real-time, precise detection of fertilizer application rates and closed-loop regulation. Full article

Recent developments in discrete probability models play a crucial role in reliability and survival analysis when lifetimes are recorded as counts. Motivated by this need, we introduce the discrete ZLindley (DZL) distribution, a novel discretization of the continuous ZL law. Constructed using a survival-function approach, the DZL retains the analytical tractability of its continuous parent while simultaneously exhibiting a monotonically decreasing probability mass function and a strictly increasing hazard rate—properties that are rarely achieved together in existing discrete models. We derive key statistical properties of the proposed distribution, including moments, quantiles, order statistics, and reliability indices such as stress–strength reliability and the mean residual life. These results demonstrate the DZL’s flexibility in modeling skewness, over-dispersion, and heavy-tailed behavior. For statistical inference, we develop maximum likelihood and symmetric Bayesian estimation procedures under censored sampling schemes, supported by asymptotic approximations, bootstrap methods, and Markov chain Monte Carlo techniques. Monte Carlo simulation studies confirm the robustness and efficiency of the Bayesian estimators, particularly under informative prior specifications. The practical applicability of the DZL is illustrated using two real datasets: failure times (in hours) of 18 electronic systems and remission durations (in weeks) of 20 leukemia patients. In both cases, the DZL provides substantially better fits than nine established discrete distributions. By combining structural simplicity, inferential flexibility, and strong empirical performance, the DZL distribution advances discrete reliability theory and offers a versatile tool for contemporary statistical modeling. Full article

This study examines whether twisted tape inserts in a pipe system can reduce pipe erosion under a liquid–solid flow regime. Three different twisted tape configurations were designed using 3D printing technology: tapes with one twist, four twists, and four twists with perforations. Experiments were performed using a PVC pipe with a carbon steel plate as the material under investigation. Slurries of water and silica sand were prepared with varying sand concentrations—1%, 3%, and 5%—to induce different erosion rates. The experimental results were backed by Computational Fluid Dynamics (CFD) using the discrete phase model (DPM) to predict particle flow and erosion attributes. Erosion trends were also tested through mass loss and paint loss tests. The analysis outcomes demonstrated that the one-twist, four-twist, and perforated four-twist tapes reduced the erosion rate by 18%, 39%, and 45%, respectively. Among the different configurations, the four-twist tape with holes reduced erosion the most. These results suggest that twisted tape inserts can control erosion, thereby increasing the service life of pipes that handle abrasive flows. Full article

The three-dimensional borehole-to-surface transient electromagnetic (BSTEM) method plays a critical role in resolving subsurface conductivity structures under complex geological conditions. However, its application is often constrained by the high computational costs associated with large-scale simulations and fine temporal resolution. In this study, a time-parallel forward modeling strategy is employed by integrating the finite volume method (FVM) with the Multigrid Reduction-in-Time (MGRIT) algorithm. Maxwell’s equations are discretized in space using unstructured octree meshes, while the MGRIT algorithm enables parallelism along the time axis through coarse–fine temporal grid hierarchy and multilevel iterative correction. Numerical experiments on synthetic and field-scale models demonstrate that the MGRIT-based solver significantly reduces computational time compared to conventional direct solvers, particularly when a large number of processors are utilized. In a field-scale hematite mine model, the MGRIT-based solver reduces the total runtime by more than 40% while maintaining numerical accuracy. The method exhibits parallel scalability and is especially advantageous in problems involving a large number of time channels, where simultaneous time-step updates offer substantial performance gains. These results confirm the effectiveness and robustness of the proposed approach for large-scale 3D TEM simulations under complex conditions and provide a practical foundation for future applications in high-resolution electromagnetic modeling and imaging. Full article

In the era of digital transformation, organizations increasingly invest in Artificial Intelligence (AI) to enhance competitiveness, yet persistent evidence shows that AI investment does not automatically translate into superior firm performance. Drawing on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study aims to explain this paradox by examining how AI-enabled dynamic capability (AIDC) is converted into performance outcomes through organizational mechanisms. Specifically, the study investigates the mediating roles of organizational data-driven culture (DDC) and organizational learning (OL). Data were collected from 254 senior managers and executives in U.S. firms actively employing AI technologies and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that AIDC exerts a significant direct effect on firm performance as well as indirect effects through both DDC and OL. Serial mediation analysis reveals that AIDC enhances performance by first fostering a data-driven mindset and subsequently institutionalizing learning processes that translate AI-generated insights into actionable organizational routines. Moreover, DDC plays a contingent moderating role in the AIDC–performance relationship, revealing a nonlinear effect whereby excessive reliance on data weakens the marginal performance benefits of AIDC. Taken together, these findings demonstrate the dual role of data-driven culture: while DDC functions as an enabling mediator that facilitates AI value creation, beyond a threshold it constrains dynamic reconfiguration by limiting managerial discretion and strategic flexibility. This insight exposes the “dark side” of data-driven culture and extends the RBV and DCT by introducing a boundary condition to the performance effects of AI-enabled capabilities. From a managerial perspective, the study highlights the importance of balancing analytical discipline with adaptive learning to sustain digital efficiency and strategic agility. Full article

Background: Cigarette burns represent a well-established forensic indicator of inflicted injury, frequently encountered in cases of domestic violence. Clinical significance: Their morphological consistency and anatomical distribution offer valuable elements for differentiating between intentional and accidental trauma. Case Presentation: In this study, we report the case of a 40-year-old woman who presented with multiple cutaneous lesions attributed to repeated assaults by her intimate partner. The forensic medical examination revealed five discrete scars characterized by sharply demarcated borders, circular to oval shapes, and dimensions ranging from 0.7 to 1.5 cm. These lesions were anatomically located in regions not typically accessible for self-infliction. To reinforce the diagnostic interpretation and assess reproducibility, a controlled experimental protocol was conducted using porcine skin matrices. Cigarette burns were recreated under variable conditions of contact pressure and exposure duration. The lesions produced on the biological substrate exhibited morphological features consistent with those observed in the patient, suggesting compatibility with cigarette-induced thermal injury. Conclusions: These findings provide circumstantial support for the forensic interpretation but must be considered within the limitations of the experimental model. This integrated approach underscores the relevance of combining clinical forensic documentation with experimental validation to support medico-legal conclusions in cases of suspected interpersonal violence. Full article

Background: The albumin–bilirubin (ALBI) grade provides an objective assessment of hepatic reserve, but the need for calculation by means of a formula has hampered its use at the bedside. This study aimed to develop simple cut-off values for ALBI grade and validate its performance in a large multi-center real-world cohort. Methods: A mathematical simulation evaluated every possible ALBI pair that falls within the Child–Pugh classification (CP) A range, discretized to 0.1 increments. Cut points for patient stratification without equation-based calculation were derived. Validation was conducted with the Chang Gung Research Database (CGRD), which contains data from 10 hospitals in Taiwan. Patients with same-day albumin and bilirubin measurements in 2024 were included. Results: Mathematical modeling identified clinically applicable cutoffs—albumin ≥ 4.4 g/dL or ≤3.5 g/dL and bilirubin ≥ 2.4 mg/dL—with further refinement at albumin 4.0 g/dL and bilirubin ≥ 1.0 mg/dL. Among 7583 CP-A patients, 82% were directly classifiable without computation, with consistent applicability across chronic liver disease and hepatocellular carcinoma (HCC) subgroups. Equation dependence increased only slightly in the HCC group, confirming robustness across disease severities. Conclusions: Simplified cutoff rules derived from mathematical modeling and validated in a multi-center cohort enable rapid recognition of ALBI grade in most CP-A patients. This approach enhances the clinical usability of ALBI and supports its integration into patient care, clinical trials, and treatment allocation. Full article

Deep generative models trained on sensitive data pose significant privacy risks, yet enforcing differential privacy (DP) in high-dimensional generators often leads to severe utility degradation. We propose Differentially Private Vector-Quantized Generation (DP-VQG), a three-stage generative framework that introduces a discrete latent bottleneck as the interface for privacy preservation. DP-VQG separates geometric structure learning, differentially private discrete latent projection, and non-private prior modeling, ensuring that privacy-induced randomness operates on a finite codebook aligned with the decoder’s effective support. This design avoids off-support degradation while providing formal end-to-end DP guarantees through composition and post-processing. We provide a theoretical analysis of privacy and utility, including explicit bounds on privacy-induced distortion. Empirically, under the privacy budget of $\epsilon =10$, DP-VQG attains Fréchet Inception Distance (FID) scores of 18.21 on MNIST and 77.09 on Fashion-MNIST, surpassing state-of-the-art differentially private generative models of comparable scale. Moreover, DP-VQG produces visually coherent samples on high-resolution datasets such as Flowers102, Food101, CelebA-HQ, and Cars, demonstrating scalability beyond prior end-to-end DP generative approaches. Full article