Search Results (1,431)

This research explores the phenomenon of plate-end (PE) debonding in reinforced concrete (RC) beams strengthened with fiber-reinforced polymer (FRP) composites. This type of failure represents a key mechanism that undermines the structural performance and efficiency of FRP reinforcement systems. Despite the widespread use of FRP in structural repair due to its high strength and corrosion resistance, PE debonding—often triggered by shear or inclined cracks—remains a major challenge. Traditional computational models for predicting PE debonding suffer from low accuracy due to the nonlinear relationship between influencing parameters. To address this, the research employs machine learning techniques and SHapley Additive exPlanations (SHAP), to develop more accurate and explainable predictive models. A comprehensive database is constructed using key parameters affecting PE debonding. Machine learning algorithms are trained and evaluated, and their performance is compared with existing normative models. The study also includes parameter importance and sensitivity analyses to enhance model interpretability and guide future design practices in FRP-based structural reinforcement. Full article

This study evaluated emergency preparedness, covering knowledge, challenges, and culturally and linguistically appropriate resources among five resettled refugee communities in Central Ohio: Afghan, Bhutanese, Congolese, Ethiopian/Eritrean, and Somali. It also explored youth perceptions of community engagement and the effectiveness of current crisis support initiatives. A mixed-methods approach was used, consisting of a cross-sectional quantitative survey of 266 adults and a qualitative 60-min focus group discussion with 10 youth from the resettled communities. Quantitative data was analyzed with descriptive statistics and Chi-square tests assessing differences in preparedness across communities. Quantitative findings showed that the Bhutanese group reported the highest familiarity with preparedness concepts (33.3%), while Afghan and Congolese communities showed significant gaps. Of the 266 overall respondents, only 39.5% had an emergency plan, and 15.8% felt extremely confident in handling emergencies. Language barriers were major challenges, along with those related to race and ethnic identity, religious practices, and cultural norms. Effective platforms for engagement included community meetings, religious sites, and social media, with text messages and phone calls preferred for emergency communication. Youth discussion highlighted key public health concerns, particularly gun violence, substance abuse, and mental health crises, with stigma and limited resources further hindering preparedness efforts. Participants emphasized the need for culturally relevant interventions and stronger community engagement. Full article

Background/Objectives: Ocular wavefront aberrations are clinically relevant for optimizing vision correction and predicting surgical outcomes. This study aimed to establish normative reference ranges for a Korean population by quantifying wavefront aberrations using a Hartmann–Shack wavefront sensor and Zernike coefficients, and to assess correlations with age, sex, and spherical equivalent (SE). Methods: Wavefront aberrations were measured in 98 Koreans (196 eyes) using a Hartmann–Shack aberrometer without cycloplegia. Five repeated measurements per eye at a 6 mm pupil size were averaged. Parameters included Zernike coefficients (Z3–Z20), higher-order aberration (HOA) root mean square (RMS, Z6–Z20), and total RMS (Z3–Z20). Associations with age, sex, and SE were assessed using univariable and multivariable linear mixed-effects models. Second-order polynomial regression assessed nonlinear relationships. Interocular symmetry was evaluated using mirror-symmetry-adjusted Spearman’s correlation and intraclass correlation coefficients (ICCs). Results: Vertical coma (Z7, 0.208 ± 0.174 μm) and spherical aberration (Z12, 0.200 ± 0.161 μm) were the largest contributors to HOA RMS. Mean HOA RMS and total RMS were 0.51 ± 0.21 μm and 3.03 ± 2.51 μm, respectively. HOA RMS increased with age (β = 0.003 μm/year, p = 0.010), whereas total RMS decreased with SE (β = −0.678 μm/D, p < 0.001). Most Zernike coefficients showed positive interocular correlations, with ICCs of 0.75 for total RMS and 0.64 for HOA RMS. Conclusions: In normal Korean eyes, HOAs increased with age and exhibited significant interocular symmetry. Vertical coma and spherical aberration were predominant components. While the pattern was similar to that in Western populations, the absolute values were greater. These normative values may aid future wavefront-guided refractive surgery and presbyopia correction procedures. Full article

We investigate the quantum dynamics of coherence in the hyperfine structure of hydrogen atoms subjected to dephasing noise, modeled using the Lindblad master equation. The effective Hamiltonian describes the spin–spin interaction between the electron and proton, with dephasing introduced via Lindblad operators. Analytical solutions for the time-dependent density matrix are derived for various initial states, including separable, partially entangled, and maximally entangled configurations. Quantum coherence is quantified through the $l_1$-norm measures, while purity is evaluated to assess mixedness. Results demonstrate that coherence exhibits oscillatory decay modulated by the dephasing rate, with antiparallel spin states showing greater resilience against noise compared to parallel configurations. These findings highlight the interplay between coherent hyperfine dynamics and environmental dephasing, offering insights into preserving quantum resources in atomic systems for applications in quantum information science. Full article

The rapid growth of the gig economy and digital platforms is challenging traditional gender roles, particularly in developing countries where structural inequalities continue to shape labor and household dynamics. Despite growing global interest in gender equity and digital inclusion, limited research has examined how gig work, digital access, and women’s income contributions interact to influence household gender dynamics within culturally conservative contexts. This study aimed to investigate the multidimensional impacts of women’s participation in gig work on time use redistribution, intra-household decision making, gender ideology, and role reversal within households in Pakistan. Using a cross-sectional survey design, data were collected from a representative sample of married couples engaged in the gig economy across urban and peri-urban areas of Pakistan. A quantitative analysis was conducted employing a combination of an analysis of variance, ordinal logistic regression, hierarchical multiple regression, and structural equation modeling to evaluate the direct and indirect relationships between constructs. The findings revealed that women’s gig work participation significantly predicted enhanced digital access, greater income contributions, and increased intra-household decision-making power. These, in turn, contributed to a measurable shift in gender ideology toward equality norms and a partial reversal of traditional gender roles, particularly in household labor division. The study concludes that the intersection of economic participation and digital empowerment serves as a catalyst for progressive gender restructuring within households. Policy implications include the need for gender-responsive labor policies, investment in digital infrastructure, and targeted interventions to support empowering women in non-traditional work roles. Full article

The grassland caterpillar Gynaephora qinghaiensis (Lepidoptera: Lymantriidae) is a dominant pest species in the alpine meadows of the Tibetan Plateau. Elucidating changes in key gene expression patterns will provide molecular insights into the adaptive evolutionary mechanisms of insects. Quantitative real-time PCR (qRT-PCR) is currently the predominant analytical methodology for assessing gene expression levels. However, variability among samples can compromise result reliability. Thus, selecting stably expressed reference genes for target gene normalization under diverse scenarios is critical. To date, suitable reference genes for G. qinghaiensis under varying experimental conditions have remained unidentified. In this study, the transcriptome data of G. qinghaiensis were obtained using the RNA-seq technique, and 13 candidate reference genes were selected. Four independent algorithms—ΔCt, geNorm, NormFinder, and BestKeeper—as well as a comprehensive online platform, RefFinder, were employed to evaluate the stability under six experimental conditions (tissues, developmental stages, sexes, temperatures, starvation, and insecticide treatments). Our findings identified the following optimal reference gene combinations for each experimental condition: RPS18, RPS15, and RPL19 for tissue samples; RPL19, RPS15, and RPL17 across developmental stages; RPS18 and RPS15 for different sexes; RPS8 and EF1-α under varying temperature conditions; RPL17 and RPL15 during starvation; and RPL19 and RPL17 following insecticide treatments. To validate the feasibility of the reference genes, we examined the expression of the target gene HSP60 in different tissues and under different temperatures. Our results established essential reference standards for qRT-PCR with G. qinghaiensis samples, laying the foundation for precise gene expression quantification in the future. Full article

This paper presents a novel adversarial Convolutional Neural Network (ConvNet) method for generating adversarial perturbations in 3D point clouds, enabling gradient-free robustness evaluation of object detection systems at inference time. Unlike existing iterative gradient methods, our approach embeds the ConvNet directly into the detection pipeline at the voxel feature level. The ConvNet is trained to maximize detection loss while maintaining perturbations within sensor error bounds through multi-component loss constraints (intensity, bias, and imbalance terms). Evaluation on a Sparsely Embedded Convolutional Detection (SECOND) detector with the KITTI dataset shows 8% overall mean Average Precision (mAP) degradation, while CenterPoint on NuScenes exhibits 24% weighted mAP reduction across 10 object classes. Analysis reveals an inverse relationship between object size and adversarial vulnerability: smaller objects (pedestrians: 13%, cyclists: 14%) show higher vulnerability compared to larger vehicles (cars: 0.2%) on KITTI, with similar patterns on NuScenes, where barriers (68%) and pedestrians (32%) are most affected. Despite perturbations remaining within typical sensor error margins (mean L2 norm of 0.09% for KITTI, 0.05% for NuScenes, corresponding to 0.9–2.6 cm at typical urban distances), substantial detection failures occur. The key novelty is training a ConvNet to learn effective adversarial perturbations during a one-time training phase and then using the trained network for gradient-free robustness evaluation during inference, requiring only a forward pass through the ConvNet (1.2–2.0 ms overhead) instead of iterative gradient computation, making continuous vulnerability monitoring practical for autonomous driving safety assessment. Full article

Background: Optical coherence tomography angiography (OCTA) enables non-invasive, high-resolution visualization of the retinal microvasculature and is increasingly utilized in pediatric ophthalmology. However, its clinical application in children is limited by the absence of age-specific normative data. Methods: In this cross-sectional study, macular vessel density (VD) and foveal avascular zone (FAZ) area were assessed in 118 healthy Caucasian children aged 4 to 17 years. OCTA scans were obtained using the OCT Topcon Triton^® device with 3 × 3 mm and 6 × 6 mm macular cubes. Vascular metrics from the superficial (SCP) and deep capillary plexuses (DCP) were analyzed in relation to demographic, refractive, biometric, and structural OCT parameters. Correlation and multivariate linear regression analyses were performed to evaluate associations. Results: Age-stratified reference percentiles for macular VD and FAZ area in SCP and DCP are presented for 118 children. Key associations included: (1) Increased macular thickness correlated with higher VD in the fovea and inner ring (SCP and DCP, all p < 0.05); (2) Thicker maculas were associated with smaller FAZ areas (SCP: r = −0.72, DCP: r = −0.58, both p < 0.001); (3) Older age was linked to reduced VD in the inner macular ring and smaller FAZ area (SCP and DCP, all p < 0.001); and (4) longer axial length correlated with lower central VD (SCP: r = −0.27, DCP: r = −0.37, both p < 0.05). No significant sex-based differences were observed. Conclusions: This study provides normative OCTA data for macular VD and FAZ area in healthy Caucasian children and identifies key associations with ocular parameters. These findings support improved diagnostic accuracy and clinical decision-making in pediatric retinal evaluation. Full article

Purpose: The present study aimed to describe the adaptation and validation process of the MuRQoL (Music Related Quality of Life questionnaire) from English to Hebrew and to describe normative data from a cohort of adults with normal hearing versus those with hearing aids or cochlear implants. Methods: After thoroughly translating and adapting to Hebrew, the participants completed the questionnaire online. We calculated the Cronbach’s alpha and McDonald’s omega scores for all scales and subscales. The construct validity of the questionnaire was evaluated using Confirmatory Factor Analysis (CFA) and the “known group” method. A total of 310 adults participated in this study. Fifty-four participants were deaf or hard of hearing, and 256 had normal hearing. Results: Internal consistency of the MuRQoL-He scales and subscales demonstrated good-to-excellent reliability. The goodness-of-fit indices for the frequency and importance scales were within acceptable standards. We found a significant difference in the frequency scale, where the normal-hearing group scores were significantly higher than those of the deaf and hard-of-hearing groups. Conclusions: The validity and reliability of the MuRQoL-He have been confirmed, indicating that it is suitable for guiding music rehabilitation for Hebrew-speaking deaf and hard-of-hearing adults. Full article

Unexplained infertility (UI) remains a diagnostic challenge affecting a significant proportion of women of reproductive age. Vascular endothelial growth factor (VEGF), a key mediator of angiogenesis and inflammation, has been implicated in reproductive and immune processes. This prospective observational study evaluated serum VEGF levels and allergen sensitization (via ALEX² macroarray) in 70 women—51 with UI and 19 fertile controls—to assess VEGF’s potential as a biomarker in UI. Median VEGF concentrations were higher in women with UI compared to fertile controls (128.6 pg/mL vs. 82.5 pg/mL), though not statistically significantly. However, sensitized women showed significantly elevated VEGF levels compared to non-sensitized peers (115.9 pg/mL vs. 85.7 pg/mL, p = 0.028), and a stepwise increase in VEGF was observed with rising allergy severity (p = 0.045). Sensitization to pet allergens, particularly cat allergen Fel d 1, was associated with the highest VEGF levels. A literature review confirmed wide variability in VEGF concentrations and the lack of standardized norms. While VEGF alone may not serve as a definitive biomarker for infertility, elevated levels may reflect an underlying inflammatory state. Our findings suggest VEGF testing could support broader clinical evaluation in women with UI, especially in the presence of allergic sensitization. Full article

Rail transit dispatch speech plays a critical role in ensuring the safety of urban rail operations. To enable automated and accurate compliance assessment of dispatch speech, this study proposes an improved deep learning model to address the limitations of conventional approaches in terms of accuracy and robustness. Building upon the baseline Whisper model, two key enhancements are introduced: (1) low-rank adaptation (LoRA) fine-tuning to better adapt the model to the specific acoustic and linguistic characteristics of rail transit dispatch speech, and (2) a novel entity-aware attention mechanism that incorporates named entity recognition (NER) embeddings into the decoder. This mechanism enables attention computation between words belonging to the same entity category across different commands and recitations, which helps highlight keywords critical for compliance assessment and achieve precise inter-sentence element alignment. Experimental results on real-world test sets demonstrate that the proposed model improves recognition accuracy by 30.5% compared to the baseline model. In terms of robustness, we evaluate the relative performance retention under severe noise conditions. While Zero-shot, Full Fine-tuning, and LoRA-only models achieve robustness scores of 72.2%, 72.4%, and 72.1%, respectively, and the NER-only variant reaches 88.1%, our proposed approach further improves to 89.6%. These results validate the model’s significant robustness and its potential to provide efficient and reliable technical support for ensuring the normative use of dispatch speech in urban rail transit operations. Full article

A parallel implementation of wavelet-based autofocus (WBA) was presented to accelerate recursive operations and reduce computational costs. WBA evaluates digital focus indices (DFIs) using first- or second-order moments of the wavelet coefficients in high-frequency subbands. WBA is generally accurate and reliable; however, its computational cost is high owing to biorthogonal decomposition. Thus, this study parallelized the Daubechies-6 wavelet and norms of the high-frequency subbands for the DFI. The kernels of the DFI computation were constructed using open sources for driving multicore processors (MCPs) and general processing units (GPUs). The standard C++, OpenCV, OpenMP, OpenCL, and CUDA open-source platforms were selected to construct the DFI kernels, considering hardware compatibility. The experiment was conducted using the MCP, peripheral GPUs, and CPU-resident GPUs on desktops for advanced users and compact devices for industrial applications. The results demonstrated that the GPUs provided sufficient performance to achieve WBA even when using budget GPUs, indicating that the GPUs are advantageous for practical applications of WBA. This study also implies that although budget GPUs are left unused, they can potentially be great resources for wavelet-based processing. Full article

Public electric vehicle (EV) charging infrastructure is crucial for accelerating EV adoption and reducing transportation emissions; however, disparities in infrastructure access have raised significant equity concerns. This review synthesizes existing knowledge and identifies gaps regarding equity in EV public charging research. Following structured review protocols, 91 peer-reviewed studies from Scopus and Google Scholar were analyzed, focusing explicitly on equity considerations. The findings indicate that current research on EV public charging equity mainly adopts geographic information systems (GIS), network optimization, behavioral modeling, and hybrid analytical frameworks, yet lacks consistent normative frameworks for assessing equity outcomes. Equity assessments highlight four key dimensions: spatial accessibility, cost burdens, reliability and usability, and user awareness and trust. Socio-economic disparities, particularly income, housing tenure, and ethnicity, frequently exacerbate inequitable access, disproportionately disadvantaging low-income, renter, and minority populations. Additionally, infrastructure-specific choices, including charger reliability, strategic location, and pricing strategies, significantly influence adoption patterns and equity outcomes. However, the existing literature primarily reflects the contexts of North America, Europe, and China, revealing substantial geographical and methodological limitations. This review suggests the need for more robust normative evaluations of equity, comprehensive demographic data integration, and advanced methodological frameworks, thereby guiding targeted, inclusive, and context-sensitive infrastructure planning and policy interventions. Full article

A fundamental limitation of maximum likelihood and Bayesian methods under model misspecification is that the asymptotic covariance matrix of the pseudo-true parameter vector ${\mathsf{\theta }}_{*}$ is not the inverse of the Fisher information, but rather the sandwich covariance matrix ${\displaystyle \frac{1}{n}}{\mathbf{A}}_{*}^{-1}{\mathbf{B}}_{*}^{\phantom{-1}}{\mathbf{A}}_{*}^{-1}$, where ${\mathbf{A}}_{*}$ and ${\mathbf{B}}_{*}$ are the sensitivity and variability matrices, respectively, evaluated at ${\mathsf{\theta }}_{*}$ for training data record ${\omega }_1,\dots ,{\omega }_n$. This paper makes three contributions. First, we review existing approaches to robust posterior sampling, including the open-faced sandwich adjustment and magnitude- and curvature-adjusted Markov chain Monte Carlo (MCMC) simulation. Second, we introduce a new sandwich-adjusted MCMC method. Unlike existing approaches that rely on arbitrary matrix square roots, eigendecompositions or a single scaling factor applied uniformly across the parameter space, our method employs a parameter-dependent learning rate $\lambda \left(\mathsf{\theta }\right)$ that enables direction-specific tempering of the likelihood. This allows the sampler to capture directional asymmetries in the sandwich distribution, particularly under model misspecification or in small-sample regimes, and yields credible regions that remain valid when standard Bayesian inference underestimates uncertainty. Third, we propose information-theoretic diagnostics for quantifying model misspecification, including a strictly proper divergence score and scalar summaries based on the Frobenius norm, Earth mover’s distance, and the Herfindahl index. These principled diagnostics complement residual-based metrics for model evaluation by directly assessing the degree of misalignment between the sensitivity and variability matrices, ${\mathbf{A}}_{*}$ and ${\mathbf{B}}_{*}$. Applications to two parametric distributions and a rainfall-runoff case study with the Xinanjiang watershed model show that conventional Bayesian methods systematically underestimate uncertainty, while the proposed method yields asymptotically valid and robust uncertainty estimates. Together, these findings advocate for sandwich-based adjustments in Bayesian practice and workflows. Full article

Objectives: This study investigated fractional anisotropy (FA) differences within key white matter tracts across patient groups stratified by Montreal Cognitive Assessment (MoCA) scores, aiming to evaluate FA’s potential as a biomarker for cognitive impairment. Methods: Seventy participants (aged 57–96 years) were categorized into high (HP, MoCA ≥ 26), moderate (MP, MoCA 18–25), and low (LP, MoCA < 18) cognitive performance groups. Diffusion Tensor Imaging (DTI) was used to obtain FA values in corticospinal tracts, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and cingulum. Statistical analyses included ANOVA and post-hoc tests. Results: Significant differences in FA values and normative percentiles were observed across cognitive groups in several tracts. Notably, the MP group exhibited significantly higher FA values in the Left Superior Longitudinal Fasciculus—Arcuate (mean FA 0.329 vs. LP 0.306, p = 0.033) and Right Superior Longitudinal Fasciculus—Arcuate (mean FA 0.329 vs. LP 0.306, p = 0.009), Left Inferior Fronto-Occipital Fasciculus (mean FA 0.308 vs. LP 0.283, p = 0.021), and Right Inferior Fronto-Occipital Fasciculus (mean FA 0.289 vs. LP 0.266, p = 0.017) compared to the LP group. Conclusions: Our findings reveal significant FA alterations across MoCA-defined cognitive groups, with moderate impairment showing higher FA than low performance. This suggests FA may reflect complex microstructural changes in early cognitive decline. While our modest sample size, particularly in the low-performance group, limits definitive conclusions, these results highlight the need for larger, multimodal studies to validate FA’s role as a sensitive, albeit complex, biomarker for cognitive impairment. Full article