Search Results (440)

The strategic positioning of heating and cooling segments within complex non-rectangular geometries has emerged as a critical engineering challenge across multiple industries in thermal management systems for electronic components. This analysis presents a numerical inspection of buoyancy-driven convective flow and thermal transport mechnisms of nanofluids in a parallelogrammic porous geometry. A single discrete heating–cooling segment has been placed along the slanting surfaces of the geometry. The mathematical model is formulated utilizing Darcy’s law, incorporating the Tiwari and Das approach to characterize the thermophysical properties of the nanofluid. The governing model equations corresponding to the physical process are solved numerically using finite-difference-based alternating direction implicit (ADI) and successive line over-relaxation (SLOR) techniques. Computational simulations are performed for various parametric conditions, including different nanoparticle volume fractions ($\varphi =0$–$0.05$), Rayleigh numbers ($Ra={10}^1$–${10}^3$), and parallelogram geometry ($\alpha$) and sidewall ($\gamma$) tilting angles ($-{45}^{°}\le \alpha \le +{45}^{°}$ and $-{45}^{°}\le \gamma \le +{45}^{°}$), while examining the effect of discrete thermal locations. The results reveal a significant decrement in thermal transfer rates with an increasing nanoparticle concentration, particularly at higher Rayleigh numbers. The skewness of the parallelogrammic boundaries is found to substantially influence flow patterns and thermal transport characteristics compared to conventional rectangular enclosures. Further, the discrete placement of heating and cooling sources creates unique thermal plumes that modify circulation patterns within the domain. The predictions suggest profound insights for optimizing thermal management systems by employing nanofluids in non-rectangular porous configurations, with potential applications in geothermal energy extraction, electronic cooling systems, and thermal energy storage devices. Full article

Generating realistic and diverse driving scenarios is essential for effective scenario-based testing and validation in autonomous driving and the development of driver assistance systems. Traditionally, parametric models are used as standard approaches for scenario generation, but they require detailed domain expertise, suffer from scalability issues, and often introduce biases due to idealizations. Recent research has demonstrated that AI models can generate more realistic driving scenarios with reduced manual effort. However, these models typically focused on single scenario types, such as cut-in maneuvers, which limits their applicability to diverse real-world driving situations. This paper, therefore, proposes a unified generative framework that can simultaneously generate multiple types of driving scenarios, including cut-in, cut-out, and cut-through maneuvers from both directions, thus covering six distinct driving behaviors. The model not only learns to generate realistic trajectories but also reflects the same statistical properties as observed in real-world data, which is essential for risk assessment. Comprehensive evaluations, including quantitative metrics and visualizations from detailed latent and physical space analyses, demonstrate that the unified model achieves comparable performance to individually trained models. The shown approach reduces modeling complexity and offers a scalable solution for generating diverse, safety-relevant driving scenarios, supporting robust testing and validation. Full article

This paper presents a comparative study on the suitability of free-boundary surface parameterization techniques for generating trajectories on 3D surfaces. The approach maps a 3D surface to a 2D parametric domain through four parameterization methods: Least-Squares Conformal Mapping, Boundary First Flattening, As-Rigid-As-Possible, and Conformal Equivalence of Triangular Meshes. Structured trajectory patterns are generated in the 2D domain and projected back to 3D. We introduce center-to-boundary geodesic deviation measure, which yields a deviation profile over the boundary loop and reflects how well central alignment is preserved under each parameterization method. The results highlight differences in distortion and geodesic preservation, reflecting the suitability of methods for path generation. Full article

The characterization of lithology within Quaternary aquifers holds significant geological importance for the protection, management, and utilization of groundwater resources, yet it continues to present considerable challenges. Indicator Kriging (IK) is a non-parametric, probability-based method of spatial interpolation. It considers the correlation and variability between data points, and its popularity stems from its alignment with geological experts’ principles. However, it still encounters issues in complex geological conditions. To address the limited capacity of conventional IK in reproducing geological variables within heterogeneous geological settings, this study develops an ordered IK method incorporating field variogram function parameters. This framework dynamically extends IK applications by integrating stratigraphic extension trends, requiring experts to formalize spatial variation trends into geological knowledge data, subsequently transformed into constraint parameters for interpolation. Estimation paths are determined via Euclidean distances between points-to-be-estimated and valid data, executing ordered IK following near-to-far and bottom-to-top principles. Results directly depict QLS formation spatial distributions or undergo expert modification for quantitative analysis, demonstrating superior integration of geological knowledge compared to empirical variogram fitting and partitioned IK estimation. The method reduces deviation from expert-interpreted spatial distributions while maintaining computational efficiency and multi-factor integration, with three case analyses confirming enhanced accuracy in lithology distribution reproduction and improved geostructural congruence in complex geological reconstruction. This approach revitalizes Kriging applications in complex geological research, synergizing domain cognition with computational efficacy to advance precision in geological characterization and support government decision-making. Full article

A compact bio-inspired terahertz wideband antenna is presented in this work. The proposed antenna is based on Viburnum tinus leaf shape, a defective ground plane, a folded-ring slot, and parasitic elements. The footprint of the proposed antenna is $0.46\times 0.18{\lambda }_g^2$ at 0.18 THz. A bandwidth of 0.536 THz (0.18–0.72 THz) is achieved with a band notch at 0.35 THz (0.3–0.36 THz). The proposed antenna has a peak gain of 5 dBi and the stable radiation patterns. The proposed antenna is validated through a finite difference time domain simulator and the equivalent circuit analysis. The results from show a good correlation. Also, an extensive parametric analysis is performed, and the comparative analysis of the proposed antenna with the existing antennas shows that the proposed antenna is compact with competitive performance metrics such as gain, efficiency, and notch-band characteristics. Therefore, the proposed antenna (hereafter referred to as VTB-A) is a promising candidate for future terahertz wireless communications (5G, 6G, and beyond) and terahertz imaging. Full article

Background: Emotionally salient music may enhance attention-focused rehabilitation, yet concurrent music plus virtual-reality programs in chronic stroke are largely untested. We assessed whether personalized emotional music stimulation (EMS) layered onto a standardized virtual reality rehabilitation system (VRRS) augments cognitive, affective, physiological, and functional outcomes. Methods: In a quasi-randomized outpatient trial, 20 adults ≥ 6 months post-ischemic stroke were allocated by order of recruitment to VRRS alone (control, n = 10) or VRRS+EMS (experimental, n = 10). Both groups performed 45 min of active VRRS cognitive training (3×/week, 8 weeks), while the EMS group received approximately 60 min sessions including setup and feedback phases. Primary outcomes were cognition and global function; secondary outcomes were intrinsic motivation, depression, anxiety, and heart rate. Non-parametric tests with effect sizes and Δ-scores were used. Results: The experimental group improved across all domains: cognition (median +4.5 points), motivation (median +54 points), depression (median −3.5 points), anxiety (median −4.0 points), heart rate (median −6.35 beats per minute), and disability (median one-grade improvement), each with large effects. The control group showed smaller gains in cognition and motivation and a modest heart-rate reduction, without significant changes in mood or disability. At post-treatment, the music group outperformed controls on cognition, motivation, and disability. Change-score analyses favored the music group for every endpoint. Larger heart-rate reductions correlated with greater improvements in depression (ρ = 0.73, p < 0.001) and anxiety (ρ = 0.58, p = 0.007). Conclusions: Adding personalized emotional music to virtual-reality attention training produced coherent, clinically relevant gains in cognition, mood, motivation, autonomic regulation, and independence compared with virtual reality alone. Full article

Mean Shift is a flexible, non-parametric clustering algorithm that identifies dense regions in data through gradient ascent on a kernel density estimate. Its ability to detect arbitrarily shaped clusters without requiring prior knowledge of the number of clusters makes it widely applicable across diverse domains. However, its quadratic computational complexity restricts its use on large or high-dimensional datasets. Numerous acceleration techniques, collectively referred to as Fast Mean Shift strategies, have been developed to address this limitation while preserving clustering quality. This paper presents a systematic theoretical analysis of these strategies, focusing on their computational impact, pairwise combinability, and mapping onto distinct stages of the Mean Shift pipeline. Acceleration methods are categorized into seed reduction, neighborhood search acceleration, adaptive bandwidth selection, kernel approximation, and parallelization, with their algorithmic roles examined in detail. A pairwise compatibility matrix is proposed to characterize synergistic and conflicting interactions among strategies. Building on this analysis, we introduce a decision framework for selecting suitable acceleration strategies based on dataset characteristics and computational constraints. This framework, together with the taxonomy, combinability analysis, and scenario-based recommendations, establishes a rigorous foundation for understanding and systematically applying Fast Mean Shift methods. Full article

This paper presents a systematic approach for rapid assessment of the performance and robustness of linear control systems through geometric analysis in the complex plane. By combining indirect performance indices within a defined zone of desired performance in the complex s-plane, a connection is established with direct performance indices, forming a foundation for the synthesis of control algorithms that ensure root placement within this zone. Analytical relationships between the complex variables s and z are derived, thereby defining an equivalent zone of desired performance for discrete-time systems in the complex z-plane. Methods for verifying digital algorithms with respect to the desired performance zone in the z-plane are presented, along with a visual assessment of robustness through radii describing robust stability and robust performance, representing performance margins under parameter variations. Through parametric modeling of controlled processes and their projections in the complex s- and z-domains, the influence of the discretization method and sampling period, as forms of a priori uncertainty, is analyzed. This paper offers original derivations for MISO systems, facilitating the analysis, explanation, and understanding of the dynamic behavior of real-world controlled processes in both the continuous and discrete-time domains, and is aimed at integration into expert systems supporting control strategy selection. The practical applicability of the proposed methodology is related to discrete control systems in energy, electric drives, and industrial automation, where parametric uncertainty and choice of method and period of discretization significantly affect both robustness and control performance. Full article

Background/Objectives: Head and neck cancers significantly affect patients’ functional and psychosocial well-being. Multidisciplinary tumor boards have a central role in optimizing treatment strategies, but the relationship between tumor characteristics, comorbidities, and quality of life (QoL) remains insufficiently explored. Methods: We conducted a retrospective study of 94 patients with head and neck cancers evaluated by the oncology committee of Coltea Clinical Hospital in 2024. QoL was assessed post-surgery using the EORTC QLQ-C30 and H&N35 questionnaires. Descriptive statistics, non-parametric tests, correlations, and multivariate regression analyses were performed to examine associations between clinical variables and QoL outcomes. Results: The cohort comprised 82 men (87.2%) and 12 women (12.8%), with a mean age of 61.5 ± 9.8 years. The most common tumor site was the larynx (43.6%). Global QoL was low (mean = 42.3, SD = 11.7), and fatigue scores were high (mean = 61.5, SD = 13.5). All EORTC domains showed non-normal distributions (Shapiro–Wilk, p < 0.05). Kruskal–Wallis analysis revealed significantly lower QoL scores in patients with metastatic adenopathy with aunknown primary (p = 0.03). Spearman’s correlation indicated a moderate negative association between Charlson Comorbidity Index and QoL (r = −0.38, p = 0.01). Multivariate regression confirmed comorbidities (β = −2.5, p = 0.02) and tumor type (metastatic adenopathy, β = −8.0, p = 0.04) as independent predictors of reduced QoL. Conclusions: Patients with advanced disease and higher comorbidity burden experience significantly poorer QoL after head and neck cancer surgery. Tumor board decisions facilitate individualized treatment planning; however, systematic integration of QoL metrics is essential to optimize both oncological and functional outcomes. Full article

In a conventional elastic beam with steady boundary stiffness, vibrational energy tends to concentrate at specific modal frequencies, often resulting in significant resonance phenomena. To address this issue, a novel control strategy is proposed in this study, in which the stiffness of boundary springs is dynamically modulated to alter the resonance characteristics of the beam. The Newmark–Beta method is employed to compute the transient response of the beam with time-varying stiffness in the time domain. A series of numerical simulations is conducted to analyze the vibration behavior of the structure under single-model frequency, multimodal frequency, narrowband, and broadband random excitations. The results indicate that time-varying stiffness effectively redistributes energy from resonance frequencies to other frequency bands, thereby suppressing resonance peaks and reducing displacement amplitudes. Furthermore, parametric analysis reveals that increasing the range of stiffness variation enhances spectral dispersion and improves vibration attenuation performance, and increasing the average stiffness level helps improve energy dispersion; however, it may lead to a slight increase in vibration response at low frequencies. Full article

In this paper, the propagation of surface gravity waves over multiple bottom-standing porous semicircular humps is examined in the absence and presence of double floating $\mathit{C}$-type detached asymmetric breakwaters. Both wave scattering and trapping phenomena are investigated within the framework of small-amplitude linear water wave theory, with the governing problem numerically solved using the multi-domain Boundary Element Method (BEM) in finite-depth water. A detailed parametric analysis is conducted to evaluate the effects of key physical parameters, including hump radius, porosity, spacing between adjacent humps, and the separation between the two $\mathit{C}$-type detached breakwaters. The study presents results for reflection and transmission coefficients, free-surface elevations, and the horizontal and vertical forces acting on the first perforated semicircular hump, as well as on the shore-fixed wall. The findings highlight the significant role of porous humps in altering Bragg scattering characteristics. For larger wavenumbers, wave reflection increases notably in the presence of a vertical shore-fixed wall, while it tends to vanish in its absence. Reflection is also observed to decrease with an increase in semicircle radius. Furthermore, as the wavenumber approaches zero, the vertical force on multiple permeable semicircles converges to zero, whereas for impermeable semicircles, it approaches unity. In addition, the horizontal force acting on the shore-fixed wall diminishes rapidly with increasing porosity of the semicircular humps. Full article

Emerging virtual reality (VR) technologies provide objective and immersive methods for assessing cognitive–motor function, particularly in elite sport. This study evaluated the reliability and validity of VR-based cognitive–motor assessments in a large sample of elite male athletes (n = 829). Ten cognitive–motor tests, delivered via Oculus Quest 2 headsets, were used, covering four domains: Balance and Gait (BG), Decision-Making (DM), Manual Dexterity (MD), and Memory (ME). A Confirmatory Factor Analysis (CFA) was conducted to establish a four-factor model and generate data-driven weights for domain-specific composite scores. The results demonstrated that the composite scores for BG, MD, ME, and a Global Cognitive–Motor (CM) score were all normally distributed. However, the DM score significantly deviated from normality, exhibiting a pronounced ceiling effect. Test–retest reliability was high across all cognitive–motor domains. In summary, VR assessments offer ecologically valid and precise measurements of cognitive–motor abilities by capitalising on high-fidelity motion tracking and standardised test delivery. In particular, the Global CM Score offers a robust metric for parametric analyses. While future work should address the DM ceiling effect and validate these tools in diverse populations, this approach holds significant potential for enhancing the precision and sensitivity of psychological and clinical assessment. Full article

The drying of nanofluid films on a surface can form various patterns and plays an important role in painting, surface patterning, and nano-fabrication processes. In this paper, a two-dimensional Kinetic Monte Carlo (KMC) model is developed based on the two-dimensional Ising model to investigate the drying patterns of nanofluids in an open domain. In the KMC model, the effective chemical potential is approximated by a linear function, in contrast to the constant value used in previous studies. This ensures that the dewetting front in the open domain consistently recedes from the edges toward the center. Simulation results show that nanoparticles, initially uniformly distributed, can assemble into branched structures that remain on the substrate after complete evaporation of the nanofluid. Furthermore, the structures observed in our study differ from the fractal cavities investigated in previous studies conducted in closed domains. A parametric study reveals that both the particle diffusion rate and the chemical potential distribution significantly influence the resulting patterns. Full article

Background and Objectives: Clinical ethical judgments are often elicited through scenario-based (vignette-based) dilemmas that guide interpretation, reasoning, and moral judgment. Despite its importance, little is known about how healthcare professionals and students respond to such scenario-based dilemmas in Eastern European settings. This study explored differences in ethical decision-making between senior medical/dental students and practicing clinicians in Romania, focusing on how scenarios-based dilemmas influence conditional versus categorical responses. Materials and Methods: A cross-sectional survey was conducted with 244 participants (51 senior students; 193 practitioners). Respondents completed a validated 35-item questionnaire presenting hypothetical ethical scenarios across seven domains: informed consent, confidentiality, medical errors, public health duties, end-of-life decisions, professional boundaries, and crisis ethics. Each scenario used a Yes/No/It depends response structure. Group comparisons were analyzed using chi-square and non-parametric tests (α = 0.05). Results: Scenario-based dilemmas elicited frequent conditional reasoning, with “It depends” emerging as the most common response (47.8%). Strong consensus appeared in rejecting concealment of harmful errors and in treating unvaccinated families, reflecting robust professional norms. Divergences arose in areas where scenario-based dilemmas emphasized system-level duties: students more often supported annual influenza vaccination (52.9% vs. 32.6%, p = 0.028) and organ purchase authorization (76.47% vs. 62. 18%, p = 0.043), while practitioners more frequently endorsed higher insurance contributions for unhealthy lifestyles (48.7% vs. 23.5%, p = 0.003). Conclusions: Scenario-based dilemmas strongly shape moral decision-making in healthcare. While students tended toward principle-driven transparency, practitioners showed pragmatic orientations linked to experience and system stewardship. To promote high-quality clinical work and align decision-making with best practice and health policy, our findings support institutional protocols for transparent error disclosure, continuing professional development in ethical communication, the possible adoption of annual influenza vaccination policies for healthcare personnel as policy options rather than categorical imperatives, and structured triage frameworks during crisis situations. These proposals highlight how scenario-based ethics training can strengthen both individual reasoning and systemic resilience. Full article

Background: Electrochemical impedance spectroscopy (EIS) is indispensable for disentangling charge-transfer, capacitive, and diffusive phenomena, yet reproducible parameter estimation and objective model selection remain unsettled. Methods: We derive closed-form impedances and analytical Jacobians for seven equivalent-circuit models (Randles, constant-phase element (CPE), and Warburg impedance (ZW) variants), enforce physical bounds, and fit synthetic spectra with 2.5% and 5.0% Gaussian noise using hybrid optimization (Differential Evolution (DE) → Levenberg–Marquardt (LM)). Uncertainty is quantified via non-parametric bootstrap; parsimony is assessed with root-mean-square error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC); physical consistency is checked by Kramers–Kronig (KK) diagnostics. Results: Solution resistance ($R_s$) and charge-transfer resistance ($R_{ct}$) are consistently identifiable across noise levels. CPE parameters $(Q,n)$ and diffusion amplitude $\left(\sigma \right)$ exhibit expected collinearity unless the frequency window excites both processes. Randles suffices for ideal interfaces; Randles+CPE lowers AIC when non-ideality and/or higher noise dominate; adding Warburg reproduces the ${45}^{\circ }$ tail and improves likelihood when diffusion is present. The $(R_{ct}+Z_W)\Vert \mathrm{CPE}$ architecture offers the best trade-off when heterogeneity and diffusion coexist. Conclusions: The framework unifies analytical derivations, hybrid optimization, and rigorous statistics to deliver traceable, reproducible EIS analysis and clear applicability domains, reducing subjective model choice. All code, data, and settings are released to enable exact reproduction. Full article