Search Results (7,587)

Performance of percutaneous coronary intervention (PCI) for bifurcation lesions involves complex decision-making informed by anatomic, hemodynamic, technical, and clinical factors. Building on the procedural foundations discussed in the companion paper in this two-part series, this second paper focuses on advanced considerations in bifurcation PCI. Factors associated with side branch (SB) compromise are discussed, including bifurcation angle and distribution of plaque location, along with strategies for SB protection and SB rewiring. Outcomes of landmark randomized controlled trials of provisional versus two-stent approaches, as well as specific two-stent techniques, are summarized. Based on these factors, an algorithmic approach for bifurcation PCI is outlined. Lastly, the use of drug-coated balloons, dedicated bifurcation stents, bioresorbable vascular scaffolds, bioadaptor stents, physiologic testing, intravascular imaging, and other emerging innovations are explored to provide a perspective on the future of bifurcation PCI. Full article

Corrosion of rock bolts in engineering exhibits random spatial distribution characteristics. To elucidate the influence mechanism of stochastic corrosion on the surface of rock bolts on the propagation behavior of ultrasonic guided waves, this study establishes a finite element model of rock bolts that incorporates stochastic corrosion characteristics. The coupled effects of corrosion depth and area ratio on guided wave propagation characteristics, time-domain response, energy distribution, and wave velocity variation are systematically investigated. Results indicate that corrosion depth and area ratio synergistically deteriorate guided wave morphology, transforming the stress field from symmetric and uniform to asymmetric and spiral. Reflections, scattering, and mode conversion induced by defects lead to a significant increase in the attenuation rate of pulse amplitude, with the two parameters governing the vertical interaction intensity and horizontal interference scope, respectively. Analysis of the Hilbert curve reveals that corrosion characteristics disrupt energy concentration. Under constant corrosion depth, an increase in area ratio disperses energy toward delayed scattered waves, while under constant area ratio, greater corrosion depth reduces the peak amplitude of the envelope curve. Overall, the energy integral exhibits an increasing trend with the degree of corrosion, whereas the peak-to-peak wave velocity shows a declining trend. The established multivariate nonlinear model accurately describes the coupled influence of corrosion parameters on wave velocity. This stochastic corrosion model overcomes the limitations of traditional simplified models and provides critical theoretical support for parameter calibration and engineering application of ultrasonic guided wave technology in the quantitative assessment of rock bolt corrosion. Full article

To handle the uncertainties brought by the increasing penetration of renewable energy sources and random loads, we design a stochastic multi-timescale distribution network reconfiguration (SMTDNR) framework to coordinate diverse scheduling resources across different timescales and develop an efficient heuristic algorithm to solve this complex NP-hard combinatorial optimization problem with high efficiency for medium- and high-voltage distribution networks. First, the SMTDNR problem, incorporating distributed renewable generators, fuel generators, energy storage systems, and controllable loads, is simplified through circular constraint linearization, Jabr relaxation, and second-order cone (SOC) relaxation techniques. Then, a one-stage multi-timescale successive branch reduction (MTSBR) algorithm is developed for distribution networks with one redundant branch, which transforms the SMTDNR problem into a stochastic multi-timescale optimal power flow (SMTOPF) problem. This is extended to a two-stage MTSBR algorithm for general networks with multiple redundant branches, which iteratively runs the proposed one-stage MTSBR algorithm. Numerical results on modified IEEE 33-bus and 123-bus distribution networks validate the superior optimality, feasibility, and computational efficiency of the proposed algorithms, particularly in scenarios of high renewable penetration and increased uncertainty, offering robust and feasible solutions where traditional methods may fail. Full article

Research shows that individuals with heart failure are 60% more likely to develop dementia because of their shared metabolic risk factors. Developing a classification model to differentiate between these two conditions effectively is crucial for improving diagnostic accuracy, guiding clinical decision-making, and supporting timely interventions in older adults. This study proposes a novel method for dementia classification, distinguishing it from its common comorbidity, heart failure, using blood testing and personal data. A dataset comprising 11,124 imbalanced electronic health records of older adults from hospitals in Chiang Rai, Thailand, was utilized. Conditional tabular generative adversarial networks (CTGANs) were employed to generate synthetic data while preserving key statistical relationships, diversity, and distributions of the original dataset. Two groups of ensemble models were analyzed: the boosting group—extreme gradient boosting, light gradient boosting machine—and the bagging group—random forest and extra trees. Performance metrics, including accuracy, precision, recall, F1-score, and area under the receiver-operating characteristic curve were evaluated. Compared with the synthetic minority oversampling technique, CTGAN-based synthetic data generation significantly enhanced the performance of ensemble learning models in classifying dementia and heart failure. Full article

Aquaculture faces challenges in reducing feed costs while promoting sustainable use of by-products. This study aimed to evaluate the effects of totally replacing soybean oil (SBO) with fish by-product oil (FBO) in the diet of Colossoma macropomum, focusing on growth performance, physiological and hepatic responses, meat composition, and economic viability. A total of 360 juveniles (9.1 ± 0.59) were distributed in a randomized design with six treatments (0–100% SBO replacement) and six replicates each, and fed to apparent satiation for 91 days. Growth performance did not differ significantly among treatments (p > 0.05), although fish receiving 40% FBO achieved the best feed conversion ratio among treatments. Hematological and biochemical analyses indicated that higher FBO levels (particularly 100%) indicating subtle yet adaptive physiological adjustments, such as moderate modulations in lipid metabolism and erythropoietic activity. Liver weight and hepatosomatic index decreased linearly with increasing FBO levels. In meat composition, FBO inclusion enhanced protein and reduced lipid contents. Although economic indicators were not statistically different (p > 0.05), offered the most favorable trade-off between biological performance and economic efficiency. These findings demonstrate that partial replacement of SBO with FBO, particularly at 40%, represents a sustainable and economically viable alternative for C. macropomum farming. Full article

This article describes a model for optimizing traffic flow control and generating traffic signal phases based on the stochastic dynamics of traffic and the percolation properties of transport networks. As input data (in SUMO), we use lane-level vehicle flow rates, treating them as random processes with unknown distributions. It is shown that the percolation threshold of the transport network can serve as a reliability criterion in a stochastic model of lane blockage and can be used to determine the control interval. To calculate the durations of permissive control signals and their sequence for different directions, vehicle queues are considered and the time required for them to reach the network’s percolation threshold is estimated. Subsequently, the lane with the largest queue (i.e., the shortest time to reach blockage) is selected, and a phase is formed for its signal control, as well as for other lanes that can be opened simultaneously. Simulation results show that when dynamic traffic signal control is used and a percolation-dynamic model for balancing road traffic is applied, lane occupancy indicators such as “congestion” decrease by 19–51% compared to a model with statically specified traffic signal phase cycles. The characteristics of flow dynamics obtained in the simulation make it possible to construct an overall control quality function and to assess, from the standpoint of traffic network management organization, an acceptable density of traffic signals and unsignalized intersections. Full article

This work presents a time-domain approach for characterizing the Ground Reaction Forces (GRFs) exerted by a pedestrian during running. It is focused on the vertical component, but the methodology is adaptable to other components or activities. The approach is developed from a statistical perspective. It relies on experimentally measured force-time series obtained from a healthy male pedestrian at eight step frequencies ranging from 130 to 200 steps/min. These data are subsequently used to build a stochastic data-driven model. The model is composed of multivariate normal distributions which represent the step patterns of each foot independently, capturing potential disparities between them. Additional univariate normal distributions represent the step scaling and the aerial phase, the latter with both feet off the ground. A dimensionality reduction procedure is also implemented to retain the essential geometric features of the steps using a sufficient set of random variables. This approach accounts for the intrinsic variability of running gait by assuming normality in the variables, validated through state-of-the-art statistical tests (Henze-Zirkler and Shapiro-Wilk) and the Box-Cox transformation. It enables the generation of virtual GRFs using pseudo-random numbers from the normal distributions. Results demonstrate strong agreement between virtual and experimental data. The virtual time signals reproduce the stochastic behavior, and their frequency content is also captured with deviations below 4.5%, most of them below 2%. This confirms that the method effectively models the inherent stochastic nature of running human gait. Full article

The Weibull distribution is widely used in reliability estimation across industries, but accurately identifying its parameters remains a challenging task. This research proposes an efficient method for estimating Weibull distribution parameters by combining the maximum likelihood method with optimization theory. First, the parameter estimation problem is formulated as an optimization problem. A constrained search space partitioning framework is introduced, leveraging parameter-specific minimum and maximum bounds for the shape, location, and scale parameters. By dividing the search space into smaller subspaces for each parameter, the method constrains the search direction, significantly reducing estimation time. To address the local optima problem common in heuristic algorithms, a randomness operator is integrated into the optimization process. The proposed constrained search space partitioning framework is implemented using a conventional g-best version of the particle swarm optimization algorithm with historical fault data. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art methods and conventional optimization-based approaches in terms of estimation accuracy and computational efficiency. Full article

Sediment erosion is a persistent problem that leads to the deterioration of hydro-turbines over time, ultimately causing blade failure. This paper analyzes the dynamics of sediment in water and its effects on a small Kaplan turbine. Flow data is obtained independently and transferred to a separate Lagrangian-based finite element code, which tracks particles throughout the computational domain to determine local impacts and erosion rates. This solver uses a random walk approach, along with statistical descriptions of particle sizes, numbers, and release positions. The turbine runner features significantly twisted blades with rounded corners, and complex three-dimensional (3-d) flow related to leakage and secondary flows. The results indicate that flow quality, particle size, concentration, and the relative position of the blades against the vanes significantly influence the distribution of impacts and erosion intensity, subsequently the local eroded mass is cumulated for each element face and averaged across one pitch of blades. At the highest concentration of 2500 mg/m³, the results show a substantial erosion rate from the rotor blades, quantified at 4.6784 × 10⁻³ mg/hr and 9.4269 × 10⁻³ mg/hr for the nominal and maximum power operating points, respectively. Extreme erosion is observed at the leading edge (LE) of the blades and along the front part of the pressure side (PS), as well as at the trailing edge (TE) near the hub corner. The distributor vanes also experience erosion, particularly at the LE on both sides, although the erosion rates in these areas are less pronounced. These findings provide essential insights into the specific regions where protective coatings should be applied, thereby extending the operational lifespan and enhancing overall resilience against sediment-induced wear. Full article

To optimize the microstructure and mechanical properties of Ti6Al4V alloys fabricated via Selective Laser Melting (SLM), this study proposes an optimization approach based on the chessboard scanning strategy. A systematic comparison of three scanning strategies—alternating, stripe, and chessboard scanning—was conducted to examine their effects on thermal input distribution, grain refinement, phase composition, and mechanical performance. Characterization results from Scanning Electron Microscopy (SEM), Electron Backscatter Diffraction (EBSD), and Transmission Electron Microscopy (TEM) revealed that the chessboard scanning strategy effectively refines the grain size to 88.64 ± 10.79 μm and increases the strengthening phase α′ content to 53.3%. Mechanical testing showed a tensile strength of 1179 ± 17 MPa (11.02% higher than stripe scanning) and elongation of 7.9 ± 0.4%. This strategy promotes random grain orientation by altering the scanning path, disrupting directional solidification, and suppressing texture formation. Microstructural mechanism analysis suggests that dislocation strengthening, increased α′ content, and grain refinement synergistically enhance both strength and ductility. These findings provide theoretical support for optimizing SLM parameters and the design of Ti6Al4V alloys’ microstructure and mechanical properties. Full article

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing factors and limited insight into underlying mechanisms, falling short of a systemic perspective and an interpretable theoretical framework. This study uses Nanxun Ancient Town as a case study to address this gap. Pedestrian trajectories were captured using temporarily installed closed-circuit television (CCTV) cameras within the scenic area and extracted using the YOLOv8 object detection algorithm. These data were then integrated with quantified environmental indicators and analyzed through Random Forest regression with SHapley Additive exPlanations (SHAP) interpretation, enabling quantitative and interpretable exploration of pedestrian dynamics. The results indicate nonlinear and context-dependent effects of environmental factors on pedestrian dynamics and that tourist flows are jointly shaped by multi-level, multi-type factors and their interrelations, producing complex and adaptive impact pathways. First, within this enclosed scenic area, spatial morphology—such as lane width, ground height, and walking distance to entrances—imposes fundamental constraints on global crowd distributions and movement patterns, whereas spatial accessibility does not display its usual salience in this context. Second, perceptual and functional attributes—including visual attractiveness, shading, and commercial points of interest—cultivate local “visiting atmospheres” through place imagery, perceived comfort, and commercial activity. Finally, nodal elements—such as signboards, temporary vendors, and public service facilities—produce multi-scale, site-centered effects that anchor and perturb flows and reinforce lingering, backtracking, and clustering at bridgeheads, squares, and comparable nodes. This study advances a shift from static and global description to a mechanism-oriented explanatory framework and clarifies the differentiated roles and linkages among environmental factors by integrating video-based trajectory analytics with machine learning interpretation. This framework demonstrates the applicability of surveillance and computer vision techniques for studying pedestrian dynamics in small-scale heritage settings, and offers practical guidance for heritage conservation and sustainable tourism management in similar historic environments. Full article

Consider regression models where the response variable Y only depends on the $p\times 1$ vector of predictors $\mathit{x}={(x_1,\dots ,x_p)}^T$ through the sufficient predictor $SP=\alpha +{\mathit{x}}^T\mathit{\beta }$. Let the covariance vector $\mathrm{Cov}(\mathit{x},Y)={\mathbf{\Sigma }}_{\mathit{x}Y}$. Assume the cases ${({\mathit{x}}_i^T,Y_i)}^T$ are independent and identically distributed random vectors for $i=1,\dots ,n$. Then for many such regression models, $\mathit{\beta }=\mathbf{0}$ if and only if ${\mathbf{\Sigma }}_{\mathit{x}Y}=\mathbf{0}$ where 0 is the $p\times 1$ vector of zeroes. The test of $H_0:{\mathbf{\Sigma }}_{\mathit{x}Y}=\mathbf{0}$ versus $H_1:{\mathbf{\Sigma }}_{\mathit{x}Y}\ne \mathbf{0}$ is equivalent to the high dimensional one sample test $H_0:\mathit{\mu }=\mathbf{0}$ versus $H_A:\mathit{\mu }\ne \mathbf{0}$ applied to ${\mathit{w}}_1,\dots ,{\mathit{w}}_n$ where ${\mathit{w}}_i=({\mathit{x}}_i-{\mathit{\mu }}_{\mathit{x}})(Y_i-{\mu }_Y)$ and the expected values $E(\mathit{x})={\mathit{\mu }}_{\mathit{x}}$ and $E(Y)={\mu }_Y$. Since ${\mathit{\mu }}_{\mathit{x}}$ and ${\mu }_Y$ are unknown, the test of $H_0:\mathit{\beta }=\mathbf{0}$ versus $H_1:\mathit{\beta }\ne \mathbf{0}$ is implemented by applying the one sample test to ${\mathit{v}}_i=({\mathit{x}}_i-\overline{\mathit{x}})(Y_i-\overline{Y})$ for $i=1,\dots ,n$. This test has milder regularity conditions than its few competitors. For the multiple linear regression one component partial least squares and marginal maximum likelihood estimators, the test can be adapted to test $H_0:{({\beta }_{i_1},\dots ,{\beta }_{i_k})}^T=\mathbf{0}$ versus $H_1:{({\beta }_{i_1},\dots ,{\beta }_{i_k})}^T\ne \mathbf{0}$ where $1\le k\le p.$ Full article

We suggest a novel strategy in the theory of elastic plane composites. The macroscopic properties are quantified, and an analytical–numerical algorithm to derive expressions for the effective constants is designed. The effective elastic constants of dispersed random composites are given by new analytical and approximate formulas where the dependence on the location of inclusions is explicitly shown in symbolic form. This essentially extends the results of previous numerical simulations for a fixed set of material constants and fixed locations of inclusions. This paper extends the analysis from Part I, which addressed dispersed random conducting composites, to the two-dimensional elastic composites. Hill’s concept of Representative Volume Element (RVE), traditionally used in elastic composites, is revised. It is rigorously demonstrated that the RVE must be a fundamental domain of the plane torus, for instance, a periodicity parallelogram, since other shapes of RVE may lead to incorrect values of the effective constants. The effective tensors of the elasticity theory are decomposed into geometrical and physical parts, represented by structural sums and material constants of the components. Novel computational methodology based on such decomposition is applied to a two-phase isotropic composite with non-overlapping circular inclusions embedded in an elastic matrix. For the first time, it is demonstrated explicitly how the effective tensors depend on the geometric probabilistic distributions of inclusions and the computational protocols involved. Analytical polynomial formulas for the effective shear modulus for the moderate concentration of inclusions are transformed using the resummation methods into practical expressions valid for all concentrations of inclusions. The critical index for the effective shear modulus is calculated from the polynomials derived for the modulus. Full article

This study presents a novel error model that distinguishes between constant and variable components of systematic error (bias) in measurement systems, particularly within clinical laboratory settings. Traditional approaches often conflict with these components, resulting in miscalculations of total error and measurement uncertainty. Through mathematical deduction and computer simulations, the authors demonstrate that the standard deviation derived from long-term quality control (QC) data includes both random error and the variable bias component, challenging its use as a sole estimator of random error. The proposed model defines the constant component of systematic error (CCSE) as a correctable term, while the variable component (VCSE(t)) behaves as a time-dependent function that cannot be efficiently corrected. The study further reveals that long-term QC data are not normally distributed, contradicting prevailing assumptions in metrology. It advocates for revised definitions in the International Vocabulary of Metrology (VIM3), emphasizing the need to distinguish between bias types determined under different measurement conditions. By applying this refined model, laboratories can enhance decision-making accuracy and more accurately estimate measurement error and uncertainty. The findings have implications beyond clinical laboratories, suggesting a paradigm shift in how systematic error is conceptualized and managed across all domains of metrology. Full article

Background: Patient monitors assist caregivers in identifying deterioration earlier by using threshold alarms. Not all of the threshold alarms necessitate immediate action, but some are a result of the triggering of a physiological event. We aim to use pattern recognition techniques to identify threshold alarm signal patterns before the onset of critical illness, thereby enabling the faster and more effective detection of clinical deterioration and supporting better clinical decision-making. Method: Secondary data from 774 pediatric patients were extracted from the IMPALA Project conducted in the High Dependency Unit (HDU) at Queen Elizabeth and Zomba Central Hospitals in Malawi. The threshold alarm data were generated from the vital signs using WHO age cut-offs and GOAL3 age cut-offs. Time-segmented alarm analysis was conducted to examine the distribution of threshold alarms around each vital sign 8 h before the onset of critical illness events. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was used to generate threshold alarm signal patterns for each signal per individual before the onset of a critical illness event. We used three machine learning approaches, random forest, support vector machine, and decision tree, to learn threshold alarm patterns in signals preceding critical illness events. Results: The total threshold alarm summed up to (3,910,083) in total for WHO and (2,041,740) for GOAL3. Temporal distributions of ECGRR, ECGHR and oxygen saturation rate (SPO2) threshold alarms were observed, revealing patterns before the onset of the critical illness events. A pattern of most threshold alarms was distributed around (40–60) for ECGRR upper threshold alarms and (0–20) for ECGRR lower threshold alarms, (80–85) for ECGHR lower threshold alarms and (140–160) for ECGHR upper threshold alarms, and (85–90) for SPO2 for death (CPR and PICU), around WHO threshold alarms. For sepsis, most of these threshold alarms were distributed around (40–50) of ECGRR upper threshold alarms and (0–20) for ECGRR lower threshold alarms, (150–180) for ECGHR upper threshold alarms, and (85) for SPO2 for WHO threshold alarms, and most of the threshold alarms had a duration of less than 30 s. The results indicate that the random forest classifier performed better in learning the threshold patterns, with an accuracy of 93% and an area under the curve of 92, compared to using the support vector machine learning model and decision tree, which had an accuracy from a classification report of 85% and 94%, with low death and sepsis precision, recall, and F1-Score. Conclusions: The analysis of threshold alarm data before critical illness events has provided valuable insights into threshold alarm patterns associated with death and sepsis. The data revealed distinct patterns in ECGRR, ECGHR, and SPO2 signals, and most of the threshold alarms were in the lower duration. The random forest classifier effectively distinguished these learned patterns around death and sepsis events compared to other algorithms. Further studies are required on the use of algorithms on all vital sign signal features in clinical settings. Full article