Search Results (1,085)

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022. Full article

This study presents the characterization of the aerodynamic forces and moments acting on irregular particles of prescribed sphericity, generated through truncated spherical harmonic expansions and immersed in a uniform flow at intermediate Reynolds numbers (1 ≤ Re ≤ 200). Particle-resolved direct numerical simulations are conducted using the commercial solver ANSYS Fluent to quantify the statistical behavior of drag, transverse lift, and transverse torque coefficients, along with the corresponding force and moment components, as a function of Reynolds number. Deviations from spherical geometry are shown to induce persistent flow asymmetries, leading to finite transverse lift and torque components even under uniform inflow conditions, effects that cannot be captured by models based on dynamically equivalent spheres. For a sphericity of 0.93, represented by six particle realizations, irregular particles exhibit mean drag values approximately 10% higher than those of spheres with the same equivalent diameter. In addition, both the magnitude and the statistical characteristics of the aerodynamic coefficients are strongly modulated by the combined effects of particle shape irregularity and flow regime. These results provide new insight into the role of geometric complexity in fluid–particle interactions and represent a step forward toward improved predictive capability beyond conventional spherical and quasi-spherical approximations. Furthermore, the present findings provide a physically grounded basis for the development of fluid–particle interaction models for irregular particles, suitable for implementation within Euler–Lagrange simulations of turbulent dispersed flows. Full article

Background and Objectives: There are known associations between bipolar disorder and obesity, but it has not been well characterized in older adults with bipolar disorder (OABD). This study aims to examine body mass index (BMI) and its clinical correlations in OABD. Materials and Methods: A secondary analysis was conducted using data from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) project, an international harmonized dataset of OABD cohorts. To examine the relationship between BMI and clinical characteristics (e.g., sex, psychiatric history, symptom severity, medication use, comorbidities), multivariable linear regression and multinomial logistic regression models with random effect for study cohort were used, with BMI as a continuous and as an ordinal (underweight vs. healthy weight vs. overweight vs. obese) dependent variable, respectively. Results: Of 1,226 OABD participants with BMI data, 405 (33.0%) were classified as overweight (BMI 25–29.99) and 462 (37.7%) as obese (BMI > 30). In linear regression models, higher BMI was associated with younger age, higher number of somatic comorbidities, and anticonvulsant use, while lower BMI was associated with lithium use. In logistic regression models, obesity was associated with cardiovascular comorbidity, musculoskeletal comorbidity and endocrine comorbidity. Conclusions: A high proportion of individuals with OABD are overweight or obese. Several demographic and clinical correlations of higher BMI were found, including younger age, higher number of medical comorbidities and anticonvulsant use. Clinicians should monitor and manage weight changes and associated comorbidities, and promote lifestyle and health interventions to minimize the risk of negative health outcomes associated with high BMI. Full article

Background/Objectives: Chronic endometritis (CE) is an underrecognized inflammatory disorder associated with infertility, recurrent pregnancy loss, and implantation failure. CD138 immunohistochemistry is widely used to detect endometrial plasma cells; however, background epithelial staining and interobserver variability may limit diagnostic precision. MUM1 (IRF4), a nuclear transcription factor expressed in plasma cells, has emerged as a potential complementary marker. We aimed to systematically evaluate the diagnostic performance of MUM1 immunohistochemistry relative to CD138-based histopathologic reference frameworks for chronic endometritis using a Bayesian meta-analytic framework. Methods: We conducted a systematic review and diagnostic test accuracy meta-analysis in accordance with PRISMA 2020 and PRISMA-DTA guidelines. MEDLINE, Embase, Scopus, and CENTRAL were searched from inception to 28 February 2026. Studies assessing MUM1 immunohistochemistry against predefined histopathologic reference frameworks for CE were eligible. Risk of bias was evaluated using QUADAS-2. A bivariate Bayesian random-effects model was applied to jointly estimate pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratio (DOR). Results: Six studies (n = 1574 women) were included in the qualitative synthesis, and four provided sufficient 2 × 2 data for quantitative pooling. The pooled sensitivity of MUM1 was 0.876 (95% credible interval (CrI): 0.536–0.976), and the pooled specificity was 0.853 (95% CrI: 0.653–0.930). The pooled positive likelihood ratio was 5.83 (95% CrI: 2.18–12.04), and the negative likelihood ratio was 0.15 (95% CrI: 0.03–0.56), corresponding to a DOR of 40.27 (95% CrI: 5.04–254.59). Credible intervals were wide, reflecting statistical uncertainty related to the limited number of studies and heterogeneity in diagnostic thresholds. Conclusions: MUM1 (IRF4) immunohistochemistry demonstrates potentially favorable comparative diagnostic performance relative to CD138-based reference frameworks, although substantial uncertainty remains due to limited and heterogeneous evidence. As an adjunctive nuclear marker, MUM1 may support histopathologic assessment of chronic endometritis; however, prospective head-to-head studies using harmonized diagnostic criteria and predefined plasma cell thresholds are required before routine implementation can be firmly recommended. Full article

This study presents a comparative Failure Modes and Effects Analysis (FMEA) of electro-hydraulic braking (EHB) and electro-mechanical braking (EMB) systems within brake-by-wire architectures. The analysis integrates both the conventional Risk Priority Number (RPN) approach and the AIAG–VDA Action Priority (AP) methodology, enabling a structured comparison of risk prioritization strategies applied to identical failure modes. A consistent system-level framework is developed to harmonize severity (S), occurrence (O), and detection (D) assessments across both architectures, allowing direct evaluation of methodological differences. The results demonstrate systematic divergences between RPN and AP approaches, particularly in high-severity scenarios, where AP provides more safety-oriented prioritization. The study further identifies key limitations of traditional RPN-based evaluation in safety-critical systems and highlights the advantages of rule-based prioritization frameworks. In addition, corrective measures are proposed and their impact on occurrence and detection ratings is quantified, illustrating practical pathways for risk reduction. Beyond methodological comparison, the work introduces a novel integration of reliability engineering with advanced manufacturing strategies, demonstrating how laser and plasma-based surface engineering can mitigate failure mechanisms by reducing occurrence and improving system robustness. The proposed approach establishes a conceptual and physically grounded bridge between system-level risk assessment and material-level optimization, contributing to the development of more reliable next-generation brake-by-wire systems. Full article

Background/Objectives: Instrument fracture remains a significant complication in endodontics. This study compared the resistance to cyclic fatigue failure between rotary and reciprocating nickel–titanium file systems, as well as differences related to file size and taper. Methods: Nineteen rotary and reciprocating file types (n = 10 per group) were evaluated in three independent test series, harmonized according to file size and system. Cyclic fatigue testing was conducted using a static model with a stainless-steel artificial canal, with an internal diameter of 0.9 mm, a 75° curvature angle, and a fixed radius for each series. Files were operated using preset programs on the X-Smart Plus, Rooter X3000, and Sendoline Endo torque-controlled motors. Time to fracture was recorded digitally, and the total number of full rotations to failure was calculated. The fractured fragments were examined with scanning electron microscopy and fractographic analysis. The data were analyzed using linear models in Stata version 19, with significance set at p ≤ 0.05. Results: Reciprocating file systems demonstrated greater time-to-fracture fatigue resistance than rotary systems. However, these differences were diminished or, in some cases, eliminated when normalized to the number of complete rotations. Fractographic analysis indicated that fractures predominantly resulted from tensile stress rather than shear forces. Conclusions: Reciprocating kinematics generally enhanced fatigue resistance compared with continuous rotation. The results suggest that fatigue resistance in machine-driven nickel–titanium instruments cannot be predicted by motion type or file design alone but reflects a complex interaction between alloy composition, heat treatment, and cross-sectional geometry. Full article

The COVID-19 pandemic highlighted both the transformative potential of mRNA vaccines and the structural challenges associated with their supply chains. Unlike traditional vaccine platforms, mRNA vaccines depend on highly specialized raw materials, including plasmid DNA (pDNA), nucleotides, enzymes, and lipid nanoparticles (LNP), that are produced by a limited number of global suppliers. These dependencies, combined with platform-specific manufacturing processes and stringent cold chain requirements, introduce vulnerabilities across production, distribution, and regulatory oversight. This narrative review examines the distinctive features of mRNA vaccine supply chains and identifies key challenges and opportunities across three interconnected domains: manufacturing systems, logistics and distribution, and regulatory governance. Drawing on literature published between January 2021 and March 2026, the review synthesizes evidence on supply chain bottlenecks revealed during the COVID-19 pandemic, including upstream raw-material dependencies, limitations in manufacturing scale-up, cold chain constraints, and regulatory fragmentation. Particular attention is given to the implications of these challenges for low- and middle-income countries, where infrastructure, technical capacity, and regulatory resources may limit participation in mRNA vaccine production and deployment. The review also highlights emerging strategies to strengthen supply chain resilience, including diversification of input suppliers, development of regional manufacturing hubs, improvements in vaccine thermostability, regulatory harmonization initiatives, and the use of digital technologies for supply chain management. By integrating insights from manufacturing, logistics, and regulatory perspectives, this study contributes to a better understanding of the structural characteristics shaping mRNA vaccine supply chains and identifies priority areas for strengthening global preparedness for future health emergencies. Full article

Open-access biodiversity repositories such as the Global Biodiversity Information Facility (GBIF) are central to contemporary conservation research, yet their heterogeneous data sources introduce quality issues and spatial sampling biases that may compromise conservation analyses. This study evaluates the spatial and taxonomic quality of GBIF occurrence data for five protected species representing mammals and vascular plants in Central Europe. A transparent data-cleaning workflow was applied, including coordinate validation, removal of duplicate and erroneous locations, and taxonomic harmonization. Spatial sampling bias was quantified using nearest neighbor distance-based metrics, enabling comparison of clustering patterns before and after data cleaning. Raw datasets exhibited strong spatial clustering across all species, with nearest neighbor ratios (NNR) ranging from 0.06 to 0.73. Data cleaning reduced the number of retained records by approximately 15–57% and increased NNR values to 0.27–0.80, indicating the removal of extreme spatial artifacts. However, NNR values remained well below unity after cleaning, demonstrating persistent non-random spatial sampling structure related to uneven sampling effort. The strongest relative improvements were observed for plant species derived from herbarium records. These results highlight the need to distinguish between data quality errors and structural spatial bias in open biodiversity data and underscore the necessity of bias-aware methods beyond standard data-cleaning procedures in conservation analyses. Full article

Background/Objectives: Pharmacogenomics (PGx) enables personalized therapy by identifying genetic variants that influence drug response. Despite the advantages of next-generation sequencing (NGS), few clinically validated, guideline-aligned panels comprehensively detect common, rare, and structurally complex pharmacogenetic variants. Methods: We developed and analytically validated Action PharmaKitDx, a targeted NGS panel covering 335 pharmacogenes, including all priority genes recommended by CPIC, DPWG, and CPNDS. Performance was assessed using Coriell HapMap and GeT-RM reference materials across multiple library preparation workflows and Illumina platforms. Clinical feasibility was evaluated in 41 patient samples from diverse specialties. Results were compared with established reference methods, including PCR-based assays, STR analysis, Sanger sequencing, and whole-exome sequencing. Results: Analytical validation: More than 99% of target bases achieved ≥30× coverage. Analytical accuracy, sensitivity, specificity, and positive predictive value exceeded 99.3%, with repeatability and reproducibility >99.7%. Concordance with GeT-RM haplotypes reached 98% after star-allele harmonization. The panel accurately detected complex variants, including CYP2D6 copy-number changes and hybrid alleles. Clinical validation: Full concordance with prior genotyping was observed in clinical samples. Beyond the initial testing indication, each sample harbored a mean of six actionable variants (range 2–10). Thirty-six rare (minor allele frequency <1%) potentially actionable variants were additionally identified. Conclusions: Action PharmaKitDx demonstrates high analytical performance and broad clinical applicability, supporting its implementation as a scalable solution for comprehensive pharmacogenetic testing and precision prescribing. Full article

Active vibration control is crucial for mitigating harmful resonant vibrations in structures subjected to harmonic loads. While antiresonant (zero-placement) methods are effective for this purpose, existing state-feedback solutions require full state measurement, and output-feedback approaches often prioritize resonance assignment over direct harmonic cancellation. This work bridges this gap by proposing a novel systematic design for a proportional–derivative (PD) output-feedback controller to achieve antiresonance for second-order linear systems with a time delay in the actuators. The method first computes a homogeneous gain solution. It then leverages the parametrization of all antiresonant solutions as a constraint within a genetic algorithm optimization. The algorithm optimizes both the stability margin, characterized by an $M_s$-disk criterion, and the number of encirclements of the critical point $(-1,0)$ in the complex plane, as assessed by the Generalized Nyquist Stability Criterion. The proposed approach provides a practical, optimized output-feedback strategy for precise rejection of harmonic disturbances, as demonstrated through a collection of numerical examples from real-world applications. The results confirm the method’s effectiveness in synthesizing stabilizing controllers that enforce antiresonance while ensuring robust stability margins. Full article

High-order Poincaré sphere (HOPS) beams have attracted tremendous interest due to their complex polarization and phase characteristics. However, manipulating the second harmonics generation (SHG) of HOPS beams is still challenging. Here, we developed a vector-coupled wave model to predict petal-shaped intensity patterns and reveal a linear correlation between petal number and topological order (n = 2 → 4). Moreover, we experimentally investigated the multidimensional regulation of SHG in HOPS beams through tailored phase-matching strategies. By employing three distinct configurations—(i) type-I phase matching, (ii) type-II phase matching, and (iii) orthogonally arranged BBO crystals based on Type-I phase matching—we establish a comprehensive framework for controlling the spatial and polarization properties of SHG in n = 2 HOPS beams. These results advance the manipulation of structured light in nonlinear optics, providing insights for optimizing applications in optical communication and polarization imaging. Full article

Multiport DC–AC converters are widely used in photovoltaic-energy storage–charging systems, but traditional two-stage schemes face challenges in circuit cost and efficiency improvements. To address this issue, a novel three-port single-stage DC–AC converter is proposed for grid-connected applications. The proposed converter integrates two DC ports and one AC port through circuit multiplexing, eliminating the high-voltage DC bus and reducing system complexity. An unfolding bridge is employed at the AC port, and full bridge circuits are used at DC ports, reducing the number of high-frequency switches. The proposed single-stage topology inherently achieves galvanic isolation and bidirectional power conversion. To achieve accurate grid current regulation and wide-range zero-voltage-switching, a multiple-phase-shift modulation method is developed to ensure a sinusoidal current waveform. The effectiveness of the proposed converter and modulation method is verified through simulation results, demonstrating a peak efficiency of 97% and a total harmonic distortion of 2.91%. Full article

Non-Intrusive Load Monitoring (NILM) enables appliance-level classification from aggregate electrical measurements and supports efficient energy management in smart buildings. However, the accuracy of existing NILM methods is often limited by the inability of conventional feature extraction techniques to capture nonlinear steady-state behavior. This study proposes a novel feature extraction framework for appliance classification, which integrates phase-space reconstruction (PSR) with 2-D Fourier series to derive geometry-based descriptors of appliance current waveforms. Unlike traditional signal-processing methods, the proposed approach utilizes the nonlinear geometric structure revealed by PSR and encodes it through Fourier descriptors, offering a discriminative, low-dimensional feature space suitable for classification using supervised machine learning algorithms. The method is evaluated on the high-resolution controlled single-appliance recordings from the COOLL dataset using the K-Nearest Neighbor (KNN) classifier. Extension to aggregated multi-appliance NILM scenarios would require additional stages such as event detection and load separation. Sensitivity analysis demonstrates that classification performance depends strongly on the choice of time delay and harmonic order, with optimal settings yielding an accuracy of up to 99.52% using KNN. The results confirm that larger time delays and a small number of harmonics effectively capture appliance-specific signatures. The findings highlight the effectiveness of PSR–Fourier-based geometric features as a robust alternative to conventional NILM feature extraction strategies. Full article

Dynamic visual cryptography (DVC) can be formulated as a discrete-time reconstruction problem for time-averaged moiré fringes generated by oscillatory transformations of periodic gratings. When implemented on digital display hardware, the continuous oscillatory motion must be realized through discrete frames, which may prevent correct reconstruction of higher-order time-averaged fringes due to refresh-rate limitations. In this work, mathematical criteria are derived to ensure the reliable reconstruction of higher-order time-averaged moiré fringes under finite refresh rate constraints. Harmonic, stochastic, and rectangular temporal waveforms are examined within a unified framework based on the number of frames per oscillation period and the discrete structure of the resulting time-averaged intensity distribution. Stochastic waveforms are shown to not guaranty reproducible fringe formation. For harmonic modulation with a 240 Hz display refresh rate and a 50 Hz oscillation frequency, only four full frames per period are obtained, which is insufficient to reconstruct the third time-averaged moiré fringe requiring at least sixteen frames per period. Rectangular waveforms satisfy the derived reconstruction conditions when the pitch of the grating, the oscillation amplitude, and the resolution of the rendered grating meet explicit constraints. These results establish quantitative parameter bounds for a mathematically consistent software-based DVC implementation on digital displays. Full article

Recent work on the evaluation of large language models emphasizes that the relevant unit of intelligence is not the artificial system alone but the human–AI hybrid. In parallel, topological and dynamical models of cognition based on Painlevé equations and non-semisimple topology propose that consciousness, intelligence, and creativity emerge from constrained long-horizon dynamics near criticality. This perspective article argues that these two research directions are deeply compatible. We show that the empirical framework for human–AI collaboration can be interpreted as a fusion process between complementary cognitive sectors: exploration (AI) and selection (human cognition). The dynamical mechanism underlying this fusion is identified with noisy phase locking between cognitive oscillators. Two independent routes to a universal $1/f$ spectral signature are developed: a geometric route through the WKB/Stokes analysis of Painlevé V confluence, and an arithmetic route through the Mangoldt function and harmonic interactions in phase-locked loops. We connect these results to the Bost–Connes quantum statistical model, whose phase transition at the pole of the Riemann zeta function provides an exact mathematical framework for the lock-in phase hypothesis of identity consolidation in AI systems. This synthesis suggests a unified research program for hybrid intelligence grounded in topology, dynamical systems, number theory, and real-world AI evaluation. Full article