Search Results (862)

Reliable weld seam perception remains challenging in industrial environments, where arc light, spatter, smoke, and varying seam geometries can seriously degrade visual sensing. These disturbances make it difficult to achieve a unified representation, accurate localization, and real-time inference at the same time. To address this problem, this paper presents an end-to-end lightweight framework for weld seam keypoint detection and tracking based on an improved SimCC. A unified three-keypoint formulation is introduced to represent different weld geometries by using one seam center point and two orientation reference points, thereby supporting a perception-to-control mapping in which position control and orientation control are decoupled. In addition, a lightweight C3k2-based backbone is designed, and a non-parametric log-domain quadratic peak-refinement decoder is proposed to alleviate the discretization-induced quantization error of SimCC classification distributions without adding model parameters. Experiments show that the proposed model contains only 1.4 M parameters, achieves 17.01 ms CPU inference latency, and obtains a detection accuracy of 1.89 px MAE. In curved weld seam tracking experiments with the integrated robotic system, it further achieves an average trajectory tracking error as low as 0.159 mm and an average orientation error of 3.738°, demonstrating its real-time accuracy and robustness for industrial welding applications. Full article

Background: Intensive care units (ICUs) provide management of critically ill patients requiring continuous monitoring and complex therapeutic interventions. The aim of this study was to analyze the clinical and biological characteristics associated with mortality in patients admitted to the intensive care unit. Methods: This retrospective observational study included 108 adult patients admitted to the Anesthesia and Intensive Care Unit of the “Sf. Apostol Andrei” Emergency County Clinical Hospital in Galați, who were in a coma at the time of admission. Demographic data, comorbidities, clinical parameters and biological biomarkers determined at admission were analyzed. Statistical analysis was performed using the SPSS program and included non-parametric tests (Mann–Whitney U), Spearman correlation analysis, multivariate logistic regression and ROC curve analysis to evaluate the predictive performance of biomarkers. Results: Hypertension (60.2%) and diabetes mellitus (35.2%) were the most common comorbidities. Comparative analysis revealed significant differences between deceased and surviving patients for several biological parameters, including leukocytes, C-reactive protein, LDH, D-dimers, INR and APTT. In multivariate analysis, LDH (OR = 0.998; p < 0.001) and APTT (OR = 0.951; p = 0.033) remained independently associated with mortality. ROC analysis revealed good discrimination capacity for LDH (AUC ≈ 0.805) and moderate performance for APTT. Conclusions: Determination of LDH and APTT at the time of admission to the ICU may provide useful information for assessing the prognosis of critically ill patients and for early stratification of mortality risk. Full article

In intelligence research, the sharing of item response data from cognitive ability assessments is often restricted by privacy concerns, while traditional parametric simulation methods frequently fail to capture complex response dependencies. This study proposes a neural network copula (NNC) framework for generating synthetic dichotomous item response data that preserves essential psychometric properties without revealing sensitive examinee information. By decoupling the modeling of marginal item probabilities from the dependence structure using a deep autoencoder and kernel density estimation, the framework accommodates the discrete nature of binary item response data while minimizing distributional assumptions. Validation against large-scale empirical data demonstrated high correspondence across multiple facets. At the data consistency level, the NNC-based synthetic data reproduced total score distributions and inter-item correlations. Psychometrically, the method yielded consistent item characteristic curve parameter estimates, item fit statistics, and test information functions. Furthermore, Monte Carlo replications demonstrated algorithmic stability and inferential precision. Full article

The SAE J2841 utility factor (UF) estimates the fraction of driving expected to occur in charge-depleting (CD) mode for plug-in hybrid electric vehicles. Emerging in-use data suggest that real-world electric usage is lower than assumed, motivating a reassessment of how charging behavior and related factors should be incorporated into the UF curve. Using trip-level data from approximately 1000 PHEVs observed over one year, we develop a charging model that captures both population-level heterogeneity in charging frequency and day-to-day characteristic temporal patterns in individual charging. The charging behavior modeling is applied to NHTS driving data to generate UF curves spanning 5 to 200 miles (8 to 322 km) of CD range. When key behavioral features are included, the resulting CD driving fractions align closely with industry-provided data. Sensitivity analysis indicates that the assumed share of habitual non-chargers is among the most influential parameters affecting the gap between the original UF and in-use data. Multiple modeling approaches were used to explore the problem and compare results, including machine learning, logistic regression, and parametric methods. Additional factors such as blended CD operation and temperature effects are discussed within a modular framework for refining J2841. These findings inform ongoing discussions on PHEV utility representation in analytical and regulatory contexts. Full article

Background and Objectives: Sepsis is a life-threatening organ dysfunction, and specific biomarkers could improve prognostic assessment in septic patients. The Sequential Organ Failure Assessment (SOFA) score is the standard tool for clinical sepsis monitoring. Recent studies highlight the need for its revision and the identification of rapid, specific, sensitive predictors of sepsis mortality. The aim of this study was to determine the significance of cardiac biomarkers alone or combined with the SOFA score for evaluating sepsis-related mortality. Materials and Methods: This is a retrospective, single-center study with a relatively small sample size of 73 septic patients (Sepsis-3 criteria) hospitalized in an intensive care unit (ICU) and intermediate care unit (IMCU). All patients had standard laboratory parameters, cardiac biomarkers, and the SOFA score available upon admission. Statistical analyses included non-parametric Mann–Whitney U test, ROC (Receiver Operating Characteristic) curve analysis, Hanley & McNeil method and Hosmer–Lemeshow goodness-of-fit test. Results: Lactate (p < 0.001) and SOFA (p < 0.001) showed the highest area under the curve (AUC) values, and all cardiac biomarkers had statistically significant AUCs (p < 0.05) for sepsis mortality prediction. A comparison of all ROC curves was conducted, but no statistically significant differences were observed. Adding hs-cTn (high-sensitivity cardiac troponin) and lactate to the SOFA score increased its AUC from 0.767 to 0.827 (p = 0.421). Conclusions: The results showed that non-survivors of sepsis had significantly higher levels of cardiac biomarkers compared to survivors. There were no statistically significant differences in the areas under the ROC curves among the three markers, or between the markers and SOFA. The addition of cardiac biomarkers to SOFA did not improve the discriminatory ability of the SOFA score. Further research with a larger sample size is required to validate and generalize the findings. Full article

The Gielis superformula is a powerful parametric tool that generates an infinite variety of natural and organic curves and surfaces through a compact set of parameters. However, classical differential geometry has lacked a unified framework for analyzing their curvature, torsion, and intrinsic geometric properties. This study addresses this gap by developing a novel superelliptic geometric framework that integrates the superformula with the differential geometry of curves and surfaces. We define the superelliptic inner and cross products, the star derivative, and the superelliptic Frenet frame to extend Euclidean and Riemannian interpretations of curvature and torsion to a more flexible parametric structure. The framework provides a uniform geometric characterization of all Gielis curves and surfaces in an intrinsic sense with respect to the proposed superelliptic metric, rather than relying on their classical Euclidean parametric representations; singular cases (e.g., $n_1<2$), which correspond to non-smooth or corner-like behavior in the Euclidean setting due to degeneracies in the radial function $r\left(t\right)$, are regularized within this framework, since the induced metric maps such Gielis-type curves to intrinsically circular geometries with constant superelliptic curvature. This unifies the entire family under a common, robust foundation while preserving orthonormality and differentiability. This superelliptic approach offers a consistent and computationally tractable model that bridges mathematical abstraction with real-world morphology, with the superformula serving as a representative example of the framework’s broad generality for diverse geometric structures. The proposed theoretical framework is further supported by computational visualization, and all figures and numerical illustrations presented in this study were generated using MATLAB R2024a, ensuring a consistent implementation of the proposed superelliptic model. Full article

(1) Background: Posterior crossbite associated with maxillary transverse deficiency is commonly managed with maxillary expansion, yet the correspondence between laboratory activation behavior and the clinical response of nickel–titanium leaf-spring expanders remains insufficiently defined; therefore, this study aimed to compare in vitro and in vivo performance of the Leaf Expander^® and to assess their agreement. (2) Methods: A retrospective sample of 15 mixed-dentition patients (7–10 years) treated at two university centers with a Leaf Expander^® (6 mm screw; 900 g) was evaluated; interpremolar (E–E), intermolar (6–6), and intercanine (C–C) distances were recorded at baseline (T0, digital models) and at follow-up visits (T1–T5, caliper measurements), while mechanical compression testing (Instron 3365) quantified force release across the activation sequence; normality (Shapiro–Wilk), parametric analyses, and Pearson correlation were used. (3) Results Posterior crossbite correction was achieved in all completed cases, with mean total increases (T0–T5) of 5.4 mm (E–E), 4.4 mm (6–6), and 6.0 mm (C–C); early expansion (T1–T0) averaged 2.5 mm at E–E, and laboratory curves showed an activation peak followed by sustained force release (~6.5–9 N) and a residual-load phase. Agreement between declared activation and clinical response was higher for E–E and 6–6 than for C–C, which showed greater variability. (4) Conclusions: These findings support the Leaf Expander^® as an effective compliance-free slow expansion device and indicate that laboratory force behavior can help interpret the clinical expansion timeline, including delayed expression after activation. Full article

Auxetic cellular materials attract increasing attention for crashworthiness and impact protection due to their negative Poisson’s ratio (NPR). However, conventional double-arrowhead auxetic honeycombs (DAHs) with straight ligaments often exhibit limited energy absorption and unstable collapse under large deformation. In this study, a curvilinear hybrid auxetic honeycomb (CHAH) is proposed by replacing straight walls with smoothly curved ligaments and embedding a circular positive Poisson’s ratio subcell to provide symmetric support. The mechanical behavior of the CHAH is investigated through a combined experimental–numerical approach. Finite element simulations are validated by quasi-static compression experiments, and a parametric study is conducted to evaluate the influence of key geometric variables on specific energy absorption (SEA) and peak crushing force (PCF). Based on the validated simulations, a multi-objective optimization framework integrating optimal Latin hypercube sampling, radial basis function surrogate modeling, and NSGA-II is employed to optimize the structural parameters. Compared with the conventional DAH under identical material and volume conditions, the CHAH exhibits significantly improved deformation stability and energy absorption capability, with SEA increasing by up to 67.06% and a more stable plateau response. In addition, SEA and PCF can be effectively tuned by varying the geometric angles (θ₁, θ₂). Full article

Non-small cell lung cancer remains one of the leading causes of cancer-related mortality worldwide, and identifying molecular markers associated with tumor progression and metastasis is important for improving patient management. This study investigated serum ELMO-3 levels in patients with NSCLC and evaluated their relationship with clinicopathological characteristics. Serum samples from 50 NSCLC patients and 20 healthy controls were analyzed. ELMO-3 concentrations were measured using an enzyme-linked immunosorbent assay. Statistical analyses included non-parametric group comparisons, receiver operating characteristic curve analysis, Kaplan–Meier survival analysis, and multivariate Cox proportional hazards regression. The mean ELMO-3 level was 0.409 ± 0.543, which was used as the cutoff value to categorize patients into low- and high-ELMO-3 groups; 76% of patients were classified as low-ELMO-3 and 24% as high-ELMO-3. The results showed that serum ELMO-3 levels did not differ significantly between NSCLC patients and healthy controls and were not associated with metastatic status. However, a significant association was observed between ELMO-3 expression status and tumor histopathology. Survival analysis demonstrated that distant metastasis and radiotherapy were significantly associated with overall survival. In multivariate analysis, age, operability, distant metastasis, and serum ELMO-3 levels were identified as independent factors associated with survival. These findings suggest that circulating ELMO-3 may have potential prognostic relevance; however, the results should be interpreted with caution and require validation in larger, independent cohorts. Full article

Introduction: Cardiovascular disease (CVD) is a leading cause of non-cancer death among cancer survivors, attributable to cardiotoxic therapies and cardiovascular risk factors. General population risk prediction tools, including ASCVD (Atherosclerotic cardiovascular disease), Framingham’s Score, and PREVENT (Predicting Risk of Cardiovascular Disease EVENTS), lack cancer-specific variables. We evaluated whether these models, even after statistical optimization, could predict cardiovascular mortality in cancer survivors. Methods: Using the National Health and Nutrition Examination Survey (NHANES) 2001–2018, linked with National Death Index (NDI) mortality data, we conducted a retrospective analysis of 634 and 429 cancer survivors, respectively, across model-specific cohorts free of baseline cardiovascular disease. Discrimination was assessed for ASCVD, Framingham Score, and PREVENT using standardized thresholds of 7.5% and 20%, as well as Youden-optimized cutoffs. Area under the curve (AUC) comparisons were performed using the DeLong non-parametric method. Results: Standard thresholds showed suboptimal discrimination across all models (AUCs: ASCVD 0.56, Framingham 0.53, PREVENT 0.64). In contrast, Youden-optimized AUCs (ASCVD: 0.68; PREVENT: 0.71; all p < 0.001, DeLong test). Optimization increased the “low-risk” group’s mortality rate from 2.8% to 4.1% (RR = 1.47), suggesting improved statistical fit came at the cost of overestimating the risk. Optimized thresholds outperformed conventional cutoffs, underscoring the necessity for recalibrated, cohort-specific risk stratification in cancer survivors. Conclusions: Standard risk scores have inadequate discrimination for cardiovascular mortality prediction in cancer survivors. Threshold recalibration improves statistical metrics but does not resolve the structural failure of these models to account for cardiotoxic exposure. Development of cardio-oncology-specific risk models incorporating oncologic exposures is therefore warranted. Full article

The shift toward Smart Cities heavily relies on adopting energy-efficiency strategies to meet ambitious decarbonization targets. However, the rebound effect, where improvements in technical efficiency are partly offset by increased energy consumption, often reduces the expected environmental and economic benefits. Traditional Marginal Abatement Cost Curves (MACC) often ignore this behavioral feedback, which can lead to an overestimation of mitigation potential. This paper introduces a data-driven probabilistic framework for assessing the influence of the rebound effect on a portfolio of urban mitigation strategies by integrating behavioral feedback into a bottom-up MACC. By combining Monte Carlo (MC) simulations to address parametric uncertainty with Bayesian Networks (BN) for conditional inference, the robustness of nine strategies is examined across residential, commercial, and transportation sectors. The results demonstrate that even a moderate rebound effect ($\eta =0.5$) causes a $10.09\%$ decrease in total net abatement, dropping from 24.86 to 22.35 tCO₂e, and significantly raises costs. Notably, the number of strictly cost-effective strategies ($MAC<0$) decreases from six to three, highlighting the fragility of certain “win–win” measures. This framework introduces the concepts of Financial Backfire Probability (FBP) and Environmental Backfire Probability (EBP) as new metrics for urban planning. These findings emphasize that rebound tolerance is a critical factor in climate policy, indicating that additional measures, such as Internet of Things (IoT)-based monitoring and demand-side management, may be necessary to prevent performance erosion amid behavioral uncertainty. Full article

To address the limitations of conventional metamaterials in thermo-mechanical coupling environments, this study proposes a multifunctional metamaterial structure through material selection and structural optimization, demonstrating stable vibration isolation performance under thermal fluctuations. The thermal deformation mechanisms and zero thermal expansion (ZTE) behavior of curved-beam unit cell are systematically examined through the chained beam constraint model (CBCM). A novel dual-zero metamaterial featuring both quasi-zero-stiffness (QZS) and ZTE characteristics is developed using curved-beam unit cell design. A parametric analysis, through finite element modeling, systematically investigated the effects of geometric parameters and material properties on the thermal expansion deformation and mechanical responses in the curved-beam unit cell structure. Furthermore, cylindrical metamaterials featuring dual-zero properties were engineered, and their deformation control mechanisms and vibration characteristic evolution across a broad temperature range were systematically studied. The simulation results indicate that while the Al–Al structure exhibits a significant resonance peak shift of up to 64.32% at 200 °C, the Al–Steel zero-stiffness design restricts this shift to only 7.72%. Furthermore, the Steel–Invar configuration demonstrates exceptional vibrational stability, with its center frequency shifting marginally from 5.58 Hz to 5.61 Hz at 200 °C. This methodology presents a viable solution for engineering metamaterials in extreme-temperature environments. Full article

This study presents a predictive manufacturing framework for customizing the biomechanical response of a 3D printed ergonomic armrest based on relaxed Voronoi metamaterials. A double curved armrest geometry was combined with parametric lattice generation, stereolithography printing in BioMed Elastic 50A resin, uniaxial compression testing of cylindrical lattice specimens, and homogenized finite element simulations using a CT derived forearm model under 15, 30, and 45 N loading. The results showed that both cell size and ligament thickness strongly affected compressive behavior, with smaller cells and thicker ligaments producing higher stiffness and earlier densification. Among the uniform configurations selected for simulation, the E-9-1.5 lattice provided the most balanced response, maintaining contact pressure below about 70 kPa up to 45 N, whereas the stiffer E-7-1.5 configuration exceeded 160 kPa and the E-7-1 configuration surpassed 100 kPa at higher load. Based on these findings, a functionally graded Voronoi concept was developed to combine a more compliant central zone with a stiffer peripheral support region while preserving conformity to the complex armrest boundary. Overall, the results show that relaxed Voronoi lattices offer a computationally efficient route toward anatomically conforming and mechanically tunable cushioning interfaces. Full article

Traditional simulators predominantly operate with position control at specific frequencies and largely neglect the appropriate imposition of accelerations on the structure. This restricts the application of realistic accelerations during fatigue testing and reduces the fidelity of tests to real road conditions. This study proposes an integrated experimental–analytical framework for motorcycle testing under laboratory conditions. Within the framework, smooth displacement reference signals are generated from noisy field-measured acceleration signals through Fourier-based harmonic curve fitting and analytic integration. Subsequently, a nonlinear adaptive backstepping control algorithm is designed to ensure accurate replication of these references within the 0–25 Hz bandwidth under parametric uncertainties. This approach provides a valuable and repeatable alternative to conventional on-road testing, ensuring that realistic road-induced accelerations are accurately imposed on the motorcycle structure during fatigue testing. Experimental signals were collected from a motorcycle on three different road surfaces, and the performance of the generated reference signals was evaluated in both the time and frequency domains. Experiments conducted on a real-time industrial controller demonstrated that the proposed controller exhibits superior tracking performance across all road profiles, achieving a Root Mean Square Error (RMSE) as low as 1.3 mm, while the Fourier-based reconstruction achieves $R^2$ values approaching 0.97. The controller maintains consistent precision and negligible performance variance despite significant differences in road characteristics, thereby offering a controlled and cost-effective laboratory simulation alternative to conventional on-road durability testing. Full article

Water distribution networks are part of critical infrastructure, and ensuring a rapid return to service after failures is vital for public health and economic resilience. While numerous studies have quantified failure rates and examined factors that influence the duration of repairs, the seasonal variability of repair time itself has received little attention. This study analyses 264 monthly observations (January 2004–December 2025) from a large urban water supply system in south-eastern Poland. We evaluate the seasonality of failure counts, average repair time per event, and the total labour hours needed to restore service. Methods include descriptive statistics, seasonal indices, non-parametric tests, kernel density estimation, parametric distribution fitting, empirical exceedance curves of monthly mean repair duration, and time-series decomposition. The results show a pronounced seasonal pattern in the number of failures and total labour hours, with peaks in winter and minima in spring, whereas the monthly mean repair duration remained stable at approximately 8 h and showed no significant seasonal variation. Among the positive-support candidate distributions, the log-normal model provided a slightly better fit than the Weibull model. Empirical exceedance analysis and non-parametric tests confirmed no significant differences in monthly mean repair duration between seasons or calendar months. Decomposition reveals a small downward trend in total repair hours after 2010. These findings provide new insights for maintenance planning and indicate that winter workload peaks are driven primarily by higher failure counts rather than by longer mean repair duration. Full article