Search Results (355)

Background/Objectives: A thorough understanding of the anatomy of the coronary sinus (CS) and its tributaries provides valuable information for selecting the optimal left ventricular lead and may even prompt reconsideration of the endovascular implantation strategy when planning cardiac resynchronization therapy (CRT). Currently, there is no personalized multislice computer tomography (MSCT) protocol for CS veins visualization that is suitable for all diverse candidates. Methods: a single-center prospective study included 74 various adult patients with recommendation class I and IIA for CRT. Prior to implantation, all participants underwent contrast MSCT to evaluate the CS veins. The first aspect of MSCT involved the administration of a test bolus to enable the automated calculation of the time-to-peak contrast opacification within the ascending aorta. The second aspect consisted of adding a fixed extra value of 20 s. The resulting sum was then used as the final delay to scan CS veins. The final cardiac acquisition was performed with prospective gating and manual phase in the range of 200–400 ms. The contrast media involved a standard iodine concentration of 300 mg I/mL, an injection rate not exceeding 4.5 mL/s, and a total contrast dose of up to 115 mL. Results: in all patients presented, all first-order CS branches were detected. The analysis found no statistically significant effect of heart rate and heart rhythm on the quality of venous visualization. The coefficient of determination (r_s²) revealed that only 28.9% of the rank variability in time-to-peak contrast opacification can be explained by Hounsfield unit. This underscores that only the test-bolus protocol with definitively calculated time delay can ensure a personalized optimal enhancement of CS veins. Conclusions: a personalized, detailed test bolus MSCT protocol for coronary sinus veins visualization is presented. Multi-vendor, multi-center studies are warranted to confirm the generalizability and external validity of the proposed MSCT protocol. Full article

Climatic variation is inherently linked with tree phenology; however, phenological triggers depend on species and habitat. We analyzed key climatic factors for the onset of secondary growth for Scots pine (Pinus sylvestris L.) at the southern limit of its distribution in Siberia. From direct observations of developing tree rings, seasonal curves of the number of cells in the cambial zone, in the cell-expansion zone, and the total number of xylem tracheids were developed over seven years with a wide variety in the phenological dates. We found that later and shorter intervals of these stages of xylogenesis were compensated by higher maximums of kinetics curves, probably due to higher temperatures and daylengths during the respective phenophases. Air temperature and soil temperature at a depth of 20 cm converged to values (mean ± SE) 6.6 ± 0.9 °C (air) and 3.7 ± 0.4 °C (soil) for a 15-day interval prior to cambial activity onset. Date of Tsoil ≥ 3.5 °C was most closely related to cambial activity onset (r = 0.99) and preceded it by 9.6 ± 1.1 days. Cumulative temperature sums were less reliable. Apparently, both air and soil temperature thresholds have to be reached for cambial division to start in this species-habitat combination. Late abundant snowfall can yield divergence between air and soil temperatures and delay the onset of xylogenesis. Full article

Traditional job shop scheduling research primarily focuses on regular performance measures such as makespan. However, in a Just-in-Time (JIT) production environment, the objective shifts toward minimizing non-regular measures, specifically the weighted sum of earliness and tardiness (E/T) penalties, as excessive earliness leads to increased work-in-process inventory costs. Addressing the JIT scheduling problem in Assembly Job-shop Scheduling Problem (AJSP) is challenging, as traditional genetic algorithms (GAs) often suffer from premature convergence due to the randomness of initial populations. This paper proposes an Improved Genetic Algorithm (IGA) based on a multi-rule collaborative initialization mechanism. The algorithm explicitly incorporates assembly tree structure constraints during the encoding phase. For population initialization, a hybrid strategy is designed by integrating forward scheduling, backward scheduling, and forward-scheduling-based delay adjustment rules to ensure both the quality and diversity of the initial solutions. Simulation experiments and ablation studies demonstrate that the proposed IGA consistently achieves lower total weighted costs across various problem scales compared to standard algorithms. The results validate that the collaborative initialization strategy effectively balances global exploration and local exploitation, providing a robust solution for AJSP under JIT constraints. Full article

Ultrasonic inspection of welded steel components remains challenging due to weld-scale material gradients, local anisotropy, attenuation, and aperture limitations. These factors severely distort both the first-arrival wavefield and the late-arriving scattered wavefield. To address this, this study presents a numerical proof of concept for three-dimensional cross-weld virtual-array coda-wave tomography (VACWT). The “virtual array” utilizes a synthetic aperture created by re-indexing sequential source–receiver records from two opposing line scans into midpoint–angle–depth coordinates. This approach enables line-based data acquisition to achieve multi-angle volumetric coverage without requiring a two-dimensional matrix array. A parameterized welded-solid benchmark model was developed, incorporating effective longitudinal and shear wave velocities, attenuation, and out-of-plane tilt fields. Four defect scenarios were evaluated: a cylindrical void, a lack-of-fusion ribbon, a porosity cluster, and an interference case. For each source–receiver path, four observables were extracted from the synthetic records: first-arrival travel time perturbations, coda wave stretching, coda decorrelation, and late-window energy ratios. These observables were then coupled into a volumetric inverse problem to separate smooth slowness variations, distributed scattering strength, and compact defect occupancy. Under the current simulation conditions, VACWT achieved smaller recovered support volumes and higher volumetric overlap compared to the delay-and-sum total focusing method (DAS-TFM), background-corrected TFM, and reverse time migration (RTM). In the interference case, applying a fixed defect-free calibration threshold yielded a centroid error of 0.48 mm, a volumetric intersection over union (IoU) of 0.856, and a false-positive volume fraction of 0.0%. While these findings serve as benchmark results rather than generalized experimental validation, the study demonstrates that late scattered wave observables provide valuable constraints for volumetric support recovery in a controlled welded-solid model. Future experimental verification on welded steel specimens with known defects remains necessary. Full article

Multimodal biometric systems have become essential components of modern electronic identity and authentication platforms where robustness under real-world degradation is critical. However, opaque face masks impose severe facial occlusion and attenuate high-frequency spectral components. Conversely, transparent face shields introduce complex specular reflections and act as an acoustic channel distortion source. Addressing these asymmetric degradation challenges, this paper proposes a reliability-aware Dynamic Score Fusion (DSF) for multimodal biometric identification. The proposed method performs sample-level reliability estimation for both face and voice modalities at the input stage. This enables sample-wise adaptive weighting of modality scores based on their estimated reliability. The framework integrates an ElasticFace-Arc backbone for face recognition with an Emphasized Channel Attention, Propagation and Aggregation—Time Delay Neural Network (ECAPA-TDNN) for speaker identification. The proposed approach is evaluated on the FaciaVox dataset, comprising face images and voice recordings acquired under multiple face-covering conditions. Experiments under the Standard to Cross-Condition Protocol (SCCP) and Multi-Condition Protocol (MCP) demonstrate that the proposed DSF consistently outperforms conventional score-level fusion methods, including Weighted Sum Fusion (WSF) and Logistic Regression Fusion (LRF). It achieves average Rank-1 accuracies of 89.6% (SCCP) and 93.7% (MCP), with gains of up to 9.3 percentage points over these baselines. The reliability estimators further demonstrate strong predictive capability, yielding Area Under the Curve (AUC) values above 0.95 for both modalities in distinguishing correctly and incorrectly identified samples under the closed-set identification setting. These findings confirm that sample-wise reliability modeling provides an effective mechanism for enhancing multimodal biometric performance under challenging mask and shield conditions, supporting the deployment of robust AI-driven electronic identification systems. Full article

Circulating fluidized bed boilers (CFBBs) are widely applied in energy, metallurgy, the chemical industry and other fields, mainly due to their high combustion efficiency and low pollution emissions. However, the CFBB combustion system, as a typical third-order plus time delay (TOPTD) system, has inherent characteristics: large inertia, significant time delays and strong coupling. Coupled with the difficulty in establishing an accurate mathematical model, traditional control methods struggle to achieve the desired control performance. Active disturbance rejection control (ADRC) has prominent advantages, such as low dependence on the controlled plant’s accurate model and strong disturbance rejection ability, but it has obvious limitations in dealing with systems with large inertia and large time delays. To address this problem, this paper proposes an input-compensated active disturbance rejection control (ICADRC) method. An input-compensated part composed of a second-order inertial link and a time delay link is introduced into the ESO input channel, which is specially optimized for the characteristics of TOPTD systems. A set of quantitative parameter tuning rules unique to ICADRC is established via the equivalent approximation method, and a dedicated MATLAB auto-tuning toolbox for ICADRC is developed for TOPTD systems. Simulation experiments are conducted on the CFBB combustion system, and the results show that the proposed ICADRC exhibits superior setpoint tracking performance, disturbance rejection performance and robustness compared with ADRC, DADRC, and SIMC-PI. Under nominal operating conditions, the ${\mathrm{IAE}}_{\mathrm{sum}}$ of ICADRC is reduced by 36.2% relative to DADRC and by 54.3% relative to SIMC-PI. Specifically, under fixed parameter perturbations, the variation amplitude of ICADRC’s performance index is only 2.1%, significantly lower than the 5.1% for DADRC, 6.1% for ADRC, and 7.3% for SIMC-PI. Full article

Decomposition of nonlinear, nonstationary multicomponent signals remains challenging for existing decomposition strategies, including frequency-based, data-driven, and subspace methods, which can suffer from mode mixing, leakage across components, and unreliable isolation of transients. Motivated by this gap, this study proposes Orthogonal Self-Similarity Decomposition (OSSD), which exploits a self-similarity structure in delay-embedded orbit geometry so that temporal organization, rather than spectrum alone, guides component construction. OSSD-Basic introduces three algorithmic novelties within a single pipeline: (1) an adaptive proxy-correlation band merging on the delay axis, (2) a dominant-component cascade that prevents energy-dominant carriers from masking weaker components, and (3) a double MGS + LS reprojection that collapses the inter-mode orthogonality index to numerical zero, regardless of merging and pruning operations. Synthetic experiments with known ground truth show that OSSD-Basic provides a parsimonious four-mode representation with exact inter-mode orthogonality (OI = 9.4 × 10⁻¹⁸), the highest reconstruction SNR among the evaluated baselines (27.14 dB), and the highest ground-truth diagonal correlation sum (3.038) among the tested methods, while using two fewer modes than EMD, VMD, and SSA. Daily streamflow forecasting on a U.S. Geological Survey discharge record further shows that augmenting OSSD-derived inputs with fractal descriptors and fractional-order differencing features yields progressive accuracy gains over the AR-ANN baseline, with R² improving from 0.855 to 0.915 at one-step-ahead and from 0.388 to 0.699 at four-step-ahead forecasting in the single-input setting, within a single-station case study on USGS 01554000. Overall, OSSD-Basic offers an interpretable multiscale decomposition with guaranteed inter-mode orthogonality and a structured feature pathway for oscillatory–transient mixtures. Full article

As 6G mobile communications evolve, Low Earth Orbit (LEO) satellite mobile edge computing (MEC) enables globally seamless computing. However, the high mobility of LEO satellites disrupts service continuity and resource stability. Existing approaches often use oversimplified models that ignore multi-beam interference and dynamic task queueing. To address this, we establish a hierarchical Geostationary Earth Orbit (GEO)–LEO synergistic architecture, where the integration is implemented by utilizing GEO satellites as stability anchors and remote cloud relays, while LEO satellites provide low-latency edge processing. We formulate fine-grained models for two-level beam-centric communication and preemptive dynamic queueing. The resulting joint task offloading and resource allocation problem is a complex mixed-integer nonlinear program (MINLP). To effectively solve this MINLP, we decouple it hierarchically: first determine discrete offloading decisions, then optimize continuous resource allocations based on them, proposing a novel framework termed G²DRL (GNN-enhanced Game-theoretic and deep reinforcement learning). Simulation results demonstrate that G²DRL significantly reduces the weighted sum of system delay and energy, showing superior convergence stability and performance over state-of-the-art DRL baselines. Full article

Many of life’s biggest dilemmas can be summed up as a tension between holding on and letting go. The very language evokes a notion of intentionality which, for the most part, has evaded scientific understanding. How might we even get a window into it? Important insights have come from a seemingly simple task: wiggling one’s fingers to and fro to the beat of a metronome. As the metronome pace increases to some critical frequency, one coordinative pattern becomes unstable and switches spontaneously to another. Such transitions are typically preceded by critical fluctuations, a predicted feature of self-organization in complex, dynamical systems. Here we address the nature and source of these fluctuations, usually assumed to be: (1) random; (2) of external origin; and (3) of fixed magnitude. We performed an experiment in which participants were instructed to oscillate their fingers in either an in-phase or anti-phase pattern in time with a metronome and instructed them to either “hold-on” or “let-go” should they feel the pattern begin to change, yielding a 2 by 2 within-subjects design. We observed that as the metronome frequency was increased from 1.00 to 3.00 Hz, fluctuations in the relative phase between the fingers were significantly altered both by the starting coordinative pattern as well as the participant’s intention to “hold it on” or “let it go”. Specifically, the intention to hold on to the anti-phase pattern delayed the spontaneous transition to in-phase, an effect that was paired with increased fluctuations beyond the critical frequency. These observations were analyzed under the extended Haken–Kelso–Bunz (HKB) model which describes the non-linear stochastic dynamics of the order parameter (relative phase) as a gradient descent on a certain potential. Our analysis, in line with experimental results, suggests that intention transforms the HKB potential not only by stabilizing unstable coordination states but also (paradoxically) by increasing fluctuations around them. Such findings may offer new interpretative light on the relation between intention and fluctuations in the coordination dynamics of living things. Full article

Thermal ablation is an effective treatment for primary liver cancer, but intraoperative assessment of ablation efficacy remains a clinical challenge. Microwave-induced thermoacoustic imaging (TAI) offers high tissue contrast based on dielectric properties, whereas conventional delay-and-sum reconstruction often yields limited contrast between ablated and normal tissue. To improve the contrast, we present a post-processing parametric imaging method that applies Nakagami statistics to thermoacoustic signal envelopes. The Nakagami shape parameter m is sensitive to thermal-ablation-induced alterations in tissue microstructural features. This work represents a new attempt to extract parametric images from thermoacoustic signal envelopes for intraoperative ablation assessment. In vitro and in vivo experiments were conducted to evaluate this Nakagami-based approach. Compared with conventional TAI, Nakagami images exhibited markedly improved contrast between the ablation zone and normal tissue. Quantitative analysis using pathological images as the gold standard demonstrated higher accuracy for Nakagami-based TAI across all measurements: 91.08% vs. 85.22% (in vitro diameter), 86.76% vs. 74.50% (in vitro area), 85.44% vs. 76.52% (in vivo diameter), and 79.22% vs. 72.72% (in vivo area). These findings suggest that Nakagami statistics-based TAI improves ablation zone characterization by capturing tissue microstructural information, showing potential as a tool for intraoperative assessment of liver ablation efficacy. Full article

The semiconductor industry has long relied on Statistical Process Control (SPC) for yield and reliability management. In early technology nodes, classic univariate tools such as Shewhart charts, cumulative sums (CUSUM), exponentially weighted moving averages (EWMA), and the Cp/Cpk exponent could effectively monitor a finite set of key variables. However, sub-5nm and emerging 3 nm technologies have fundamentally changed the statistical environment. Advanced patterning, high-aspect-ratio etching, atomic layer deposition (ALD), chemical-mechanical polishing (CMP), and novel materials have drastically narrowed the process window. At these scales, nanometer-level deviations in critical dimensions (CD), overlay, or surface roughness can significantly impact yield. Simultaneously, modern wafer fabs generate massive amounts of high-frequency sensor data and high-dimensional metrology data. Traditional SPC assumptions—such as independence, normality, low dimensionality, and stationarity—often do not hold. Semiconductor data exhibits: (i) extremely high-dimensionality and strong intervariate correlations; (ii) a hierarchical structure encompassing fab → tooling → chamber → recipe → batch → wafer → field; and (iii) metrological delays and sampling limitations leading to incomplete and asynchronous observations. To address these challenges, this paper reviews advanced statistical methods applicable to wafer fabrication. These methods include multivariate statistical process control (MSPC) approaches such as Hotelling T² statistics, PCA/PLS combining T² and Q statistics, contribution diagnostics, time-series drift and change point detection, and Bayesian hierarchical modeling for uncertainty-aware monitoring in data-limited scenarios. Furthermore, we discuss how to integrate these methods with fault detection and classification (FDC), line-to-line monitoring (R2R), advanced process control (APC), and manufacturing execution systems (MES). This paper focuses on scalable, interpretable, and maintainable implementations that transform statistical analysis from a passive monitoring tool into an active component of data-driven fab control. Full article

Integrated sensing and communications (ISAC) requires tight coordination between spatial signal design and multiple-access strategies to balance communication throughput and sensing accuracy under shared spectral and hardware constraints. However, existing ISAC frameworks with rate-splitting multiple access (RSMA) typically rely on fixed antenna arrays and decoupled optimization, which fundamentally limit their ability to adapt to fast channel variations and dynamic sensing requirements. This paper introduces a fluid antenna-enabled RSMA-assisted ISAC architecture, in which movable antenna ports are exploited as a new spatial degree of freedom to enhance adaptability in both communication and sensing operations. Fluid antenna systems (FAS) are deployed at both the base station and user terminals, allowing dynamic port selection that reshapes the effective channel and sensing beampattern in real time. We formulate a joint sum-rate maximization problem subject to explicit sensing-quality constraints, capturing the coupled impact of antenna port selection, RSMA rate allocation, and multi-beam transmit design. The proposed framework maximizes the communication sum-rate while ensuring that the sensing functionality satisfies a predefined sensing quality constraint. This constraint-based ISAC formulation guarantees that sufficient sensing power is directed toward the target while optimizing communication performance. The resulting optimization involves strongly coupled discrete and continuous decision variables, rendering conventional optimization methods ineffective. To address this challenge, a hierarchical deep reinforcement learning (HDRL) framework is developed, where an upper-layer deep Q-network (DQN) determines discrete antenna port selection and a lower-layer twin delayed deep deterministic policy gradient (TD3) algorithm optimizes continuous beamforming and rate-splitting parameters. Numerical results demonstrate that the proposed approach significantly improves system performance, achieving higher communication sum-rate while satisfying sensing requirements under dynamic propagation conditions. Full article

Sequential change detection is a fundamental problem in statistics and signal processing, with the CUSUM procedure widely used to achieve minimax detection delay under a prescribed false alarm rate when pre- and post-change distributions are fully known. However, in many practical settings, raw observations cannot be shared with a trusted central curator, and privacy must be enforced at the data source, which prevents the computation of exact CUSUM statistics. We therefore introduce a local differentially private (DP) variant called LDP-CUSUM, which first applies a local DP mechanism to transform the raw data into privatized observations and then applies a CUSUM procedure to detect the change. We derive closed-form bounds on the average run length to false alarm and on the worst-case average detection delay, explicitly characterizing the tradeoff among privacy level, false alarm rate, and detection efficiency. Numerical simulations and a real-data case study were conducted to demonstrate the detection efficiency of our proposed LDP-CUSUM across various scenarios. Full article

Ultrasound imaging is a crucial tool for guiding radiation therapy, particularly for cancers such as pancreatic cancer, where tumors exhibit respiration-induced motion. While flexible ultrasound transducers offer improved anatomical conformity and reduced compression-induced distortion compared to rigid probes, their variable geometry presents significant challenges for conventional beamforming. In this study, we investigate a deep learning-based beamforming framework that directly predicts delayed RF data from raw RF input, bypassing explicit transducer shape estimation and traditional delay-and-sum computations. Building upon an artificial curvature simulation strategy, we systematically analyze the impact of curvature-induced variation and inherent RF noise on model performance and generalizability. We further introduce frequency-domain analysis to quantify RF-level signal variation that may not be apparent in spatial-domain image comparisons. Our results demonstrate that although noise-augmented training improves prediction consistency, reconstruction performance remains limited under the current prototype noise conditions. These findings highlight the importance of RF data diversity and noise characterization in developing clinically robust deep learning beamformers for flexible transducer-based ultrasound-guided radiation therapy. Full article

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article